Target recognition and payment system, method, device and equipment based on two-dimensional code

Abstract

This specification provides a target recognition and a QR code-based payment system, method, apparatus, and device. The solution includes: a target recognition system comprising: a wide-angle camera for acquiring environmental images; a movable camera with a smaller field of view than the wide-angle camera and capable of adjusting its shooting angle; and a processor communicatively connected to the wide-angle camera and the movable camera, configured to: firstly detect a QR code target in the environmental image acquired by the wide-angle camera; then, based on the position of the QR code target in the environmental image, send a control signal to the movable camera to drive it to adjust its shooting angle so that the QR code target enters its field of view; and further, obtain the QR code target's recognition information from the target image acquired by the movable camera, the recognition information being used to trigger a payment process.

Description

Technical Field

[0001] This specification relates to the field of image processing technology, and more particularly to target recognition systems and QR code-based payment systems. This specification also relates to target recognition methods and QR code-based payment methods, target recognition devices and QR code-based payment devices, and a computing device. Background Technology

[0002] QR code recognition technology has been widely used in mobile payments, identity verification, and information retrieval. With the popularization of smart terminal devices and the development of IoT technology, QR code recognition systems are increasingly being integrated into various mobile platforms and automated devices to meet the recognition needs of different scenarios. In scenarios such as parking lot payments, highway toll collection, and gas station payments, users need to scan QR codes to complete payment operations.

[0003] As application scenarios continue to expand, higher demands are being placed on the automation level, recognition distance, and recognition success rate of QR code recognition. Especially in driving scenarios or mobile platform applications, users find it difficult to hold their devices close to the QR code, requiring systems capable of automatically recognizing QR codes from greater distances and under more complex motion conditions. Therefore, a QR code recognition solution that can adapt to these scenarios is needed. Summary of the Invention

[0004] In view of this, one or more embodiments of this specification provide target recognition and QR code-based payment systems, methods, apparatuses, and devices to provide a new QR code recognition scheme.

[0005] According to a first aspect of one or more embodiments of this specification, a target recognition system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger the payment process.

[0006] According to a second aspect of one or more embodiments of this specification, a QR code-based payment system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The payment-related information of the QR code target is obtained from the target image captured by the movable camera; The payment process is triggered based on the payment-related information.

[0007] According to a third aspect of one or more embodiments of this specification, a target recognition system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust its shooting angle so that the QR code target enters the field of view of the movable camera; and The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger business processing.

[0008] According to a fourth aspect of one or more embodiments of this specification, a target recognition method is provided, comprising: Acquire environmental images captured by a wide-angle camera; Detect QR code targets from the environmental image; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger the payment process.

[0009] According to a fifth aspect of one or more embodiments of this specification, a QR code-based payment method is provided, comprising: Acquire environmental images captured by a wide-angle camera; Detect QR code targets from the environmental image; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The payment-related information of the QR code target is obtained from the target image captured by the movable camera; The payment process is triggered based on the payment-related information.

[0010] According to a sixth aspect of one or more embodiments of this specification, a target identification device is provided, comprising: The environmental image acquisition module is used to acquire environmental images captured by the wide-angle camera; A QR code detection module is used to detect QR code targets from the environmental image; A control signal sending module is used to send a control signal to a movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The QR code recognition module is used to obtain the recognition information of the QR code target from the target image captured by the movable camera, and the recognition information is used to trigger the payment process.

[0011] According to a seventh aspect of one or more embodiments of this specification, a QR code-based payment device is provided, comprising: The environmental image acquisition module is used to acquire environmental images captured by the wide-angle camera; A QR code detection module is used to detect QR code targets from the environmental image; A control signal sending module is used to send a control signal to a movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; A QR code recognition module is used to obtain payment-related information of the QR code target from the target image captured by the movable camera; The payment triggering module is used to trigger the payment process based on the payment-related information.

[0012] According to an eighth aspect of one or more embodiments of this specification, a computing device is provided, including a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein the processor, when executing the computer instructions, implements the steps of the target identification method or the QR code-based payment method.

[0013] One embodiment of this specification achieves at least the following beneficial effects: By cooperating with a wide-angle camera and a movable camera, the wide-angle camera first captures environmental images and detects QR code targets using its large field of view. Then, based on the target's position in the environmental image, the movable camera adjusts its shooting angle, bringing the QR code target into the smaller field of view of the movable camera. This allows for the acquisition of a clear target image and the extraction of recognition information to trigger the payment process. This architecture enables the system to automatically search for and lock onto QR code targets over a large area. Even if the target is far away or its position is not fixed, high-quality images can be obtained through precise alignment of the movable camera. This effectively improves the automation and success rate of QR code recognition, provides reliable information input for subsequent payment processes, reduces the need for manual alignment by users, and enhances ease of use. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic diagram illustrating the application scenarios of the target recognition system and the QR code-based payment system provided in the embodiments of this specification; Figure 2 A schematic diagram of a target recognition system provided in the embodiments of this specification; Figure 3 A flowchart illustrating a target recognition method provided in an embodiment of this specification; Figure 4 A schematic flowchart illustrating a QR code-based payment method provided in this specification. Figure 5 This is a flowchart illustrating a QR code recognition and payment solution for vehicles in a practical application scenario provided in the embodiments of this specification. Figure 6 The embodiments provided in this specification correspond to Figure 3 A schematic diagram of the structure of a target recognition device; Figure 7 The embodiments provided in this specification correspond to Figure 4 A schematic diagram of the structure of a QR code-based payment device; Figure 8 A structural block diagram of a computing device provided according to an embodiment of this specification is shown. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0017] This specification uses specific terms to describe embodiments thereof. Terms such as "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of those different embodiments or examples, without contradiction.

[0018] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “an,” “an,” “the,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification includes any or all possible combinations of one or more associated listed items.

[0019] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded.

[0020] Although the terms "first," "second," etc., may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, "first" may also be referred to as "second," and similarly, "second" may also be referred to as "first," without departing from the scope of one or more embodiments of this specification. Ordinal numbers such as "first," "second," etc., do not necessarily indicate order; often they are used to facilitate the distinction of objects. For example, "first server" and "second server" usually refer to two servers. To distinguish these two servers, they are described as "first server" and "second server." Of course, sometimes these two servers may be the same server.

[0021] Depending on the context, the word "if" as used here can be interpreted as "when," "when," or "in response to determination."

[0022] In this specification, unless explicitly stated otherwise, "receiving and sending data" does not necessarily mean direct receiving and sending; it can also mean indirect receiving and sending. For example, A receiving data sent by B can be understood as A directly receiving the data sent by B, or it can be understood as A indirectly receiving the data sent by B through other entities such as C. Similarly, B sending data to A can be understood as B sending the data directly to A, or it can be understood as B indirectly sending the data to A through other entities such as C. Here, C can be one entity, or it can be two or more entities.

[0023] In this specification, unless explicitly stated otherwise, the relationships between structures can be direct or indirect. For example, when describing "A is connected to B," unless it is explicitly stated that A and B are directly connected, it should be understood that A can be directly connected to B or indirectly connected to B. Similarly, when describing "A is on top of B," unless it is explicitly stated that A is directly above B (AB is adjacent and A is above B), it should be understood that A can be directly above B or indirectly above B (AB is separated by other elements, and A is above B). And so on.

[0024] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. The collection, use and processing of related data shall comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation entry points shall be provided for users to choose to authorize or refuse.

[0025] In related technologies, when paying tolls in scenarios such as parking lots and highway toll stations, users typically need to hold their mobile phones and scan QR codes at close range using the phone's camera. This method faces some practical challenges in driving scenarios: drivers need to hold their phones to find and align the QR code while the vehicle is moving or has come to a stop, which can distract them from driving; at the same time, factors such as motion blur and changes in lighting in the in-vehicle environment affect the success rate of QR code recognition in the image, sometimes requiring users to try multiple times to complete the scan, and in some cases even requiring them to get out of the car to do so; during peak hours, the accumulated time required for a single vehicle to scan the code can affect the efficiency of passage at entrances and exits.

[0026] To address the shortcomings in related technologies, embodiments of this specification provide a target recognition system and a QR code-based payment system, aiming to achieve long-distance, high-success-rate, and non-intrusive automatic recognition of parking codes using vehicle-mounted sensors and cameras.

[0027] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This diagram illustrates the application scenarios of the target recognition system and the QR code-based payment system provided in the embodiments of this specification.

[0029] like Figure 1 As shown, the system can be applied to mobile carriers (such as vehicles) to automatically recognize QR codes and complete payments in scenarios such as parking lots and toll stations.

[0030] exist Figure 1In the scenario shown, a vehicle is approaching the tollbooth exit. The system includes a wide-angle camera, a movable camera, and a processor (the connection between the processor and the camera is not shown in the figure). The wide-angle camera can be fixedly mounted inside the vehicle (e.g., on the windshield) or outside the vehicle (e.g., on the side of the vehicle) to capture environmental images with a wide field of view, such as covering the road in front of the vehicle, the toll gate, and the surrounding area. It is mainly used for large-scale target search and environmental perception. In this embodiment, the movable camera is a gimbal camera with at least two degrees of freedom of rotation (pitch and yaw) and optionally optical zoom capability. It is used for precise aiming and high-definition image capture of specific targets under the control of the processor.

[0031] In a typical application process, as a vehicle approaches the payment gate, a wide-angle camera captures an environmental image including the distant gate. After the processor detects a QR code target affixed to the gate pillar or payment window within this image, it sends a control signal to the PTZ camera based on the QR code's position in the image. This causes the PTZ camera to rotate and align with the QR code, then uses optical zoom to magnify the QR code to a sufficiently clear size. Subsequently, the high-resolution image of the target captured by the PTZ camera is used to extract payment-related information from the QR code and trigger the subsequent payment process.

[0032] It should be noted that, Figure 1 The example shown uses a vehicle as a mobile carrier, but the application scenarios of this invention are not limited to this. Those skilled in the art will understand that the system can also be applied to other mobile platforms, such as drones scanning QR codes at delivery stations to complete service fee payments, or service robots scanning QR codes at merchants to complete order settlements. Regardless of the carrier on which it is applied, Figure 1 The dual-camera linkage architecture and its working principle shown are applicable to both.

[0033] In the embodiments of this specification, a target recognition system and a QR code-based payment system are provided. This application also relates to a target recognition method and a QR code-based payment method, a target recognition device and a QR code-based payment device, and a computing device, which will be described in detail in the following embodiments.

[0034] Figure 2 This is a schematic diagram of a target recognition system provided in an embodiment of this specification.

[0035] like Figure 2 As shown, a target recognition system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger the payment process.

[0036] Reference Figure 2 This specification provides a target recognition system in several embodiments. The system includes a wide-angle camera, a movable camera, and a processor. The wide-angle camera is used to acquire environmental images; its large field of view covers a wide area, making it suitable for preliminary target searching. The movable camera has a smaller field of view than the wide-angle camera and is capable of adjusting its shooting angle, for example, by rotating a gimbal to adjust its shooting direction or by adjusting its focal length through optical zoom, thereby enabling precise observation of specific targets. The processor is communicatively connected to both the wide-angle camera and the movable camera, controlling their operation and processing the acquired image data.

[0037] The field of view (FOV) is the angle between the two edges of an optical instrument (camera) at which the image of the target object can pass through the lens to its maximum extent. The FOV describes the physical characteristics of the lens. The size of the FOV determines the field of view of the optical instrument (camera); a larger FOV results in a wider field of view. A shorter focal length results in a larger FOV, and a longer focal length results in a smaller FOV.

[0038] like Figure 2 As shown, the environmental image refers to the image captured by the wide-angle camera, which includes a large field of view and is used for initial search and location of the QR code target. The target image refers to the image captured by the movable camera, which has a smaller field of view but higher resolution and is used to obtain detailed information about the QR code target for recognition. The control signal is the instruction sent by the processor to the movable camera to drive it to adjust the shooting angle. The recognition information refers to the data content parsed from the QR code, which can be used to trigger subsequent business processing (payment process).

[0039] In some embodiments of this specification, a movable camera refers to a camera device capable of adjusting its shooting angle. By adjusting the shooting angle, a movable camera can aim its field of view at a specific target, thereby acquiring a high-definition image of the target. The specific implementation of the movable camera can be selected according to the application scenario and accuracy requirements; several exemplary implementation methods are provided below.

[0040] In one specific embodiment, the movable camera is a gimbal camera. This gimbal camera possesses at least two degrees of electrical rotation capability: pitch and yaw. It is configured to rotate in at least these two degrees of freedom under the control of the processor, enabling precise aiming and dynamic tracking of specific targets under processor control. Further, the gimbal camera includes a horizontal rotation mechanism, a vertical rotation mechanism, and a decoder. The horizontal rotation mechanism drives the camera to rotate horizontally, and the vertical rotation mechanism drives it to rotate vertically. The decoder is communicatively connected to the processor, receiving control commands from the processor and driving the horizontal and vertical rotation mechanisms to perform corresponding rotation actions according to the commands. For example, when the processor needs to align the camera with a QR code target located in the upper right corner of the environmental image, it can generate a control signal containing the desired attitude angle. After receiving this signal, the decoder drives the horizontal and vertical rotation mechanisms to move in tandem, causing the camera to rotate to the target direction. This embodiment is suitable for scenarios requiring a wide range of multi-angle adjustments to the shooting perspective.

[0041] In another embodiment, the movable camera is mounted on an electrically operated slide rail, allowing it to change its shooting position by moving horizontally. For example, the camera can slide along a linear guide rail to capture images of the target from different positions. This embodiment is suitable for scenarios where it is necessary to change the shooting baseline or bypass obstructions.

[0042] Those skilled in the art will understand that the above embodiments are merely illustrative and not intended to limit the invention. Any camera structure capable of adjusting the shooting angle, whether through physical rotation, mechanical movement, or other means, can be applied to some embodiments of this specification, as long as it can achieve the function of bringing the target into the field of view. The specific implementation method adopted can be determined comprehensively based on factors such as the accuracy requirements, cost budget, and installation space of the actual application scenario.

[0043] In some embodiments of this specification, the environmental image refers to an image captured by a wide-angle camera, encompassing a large field of view, used for initial searching and locating the QR code target. The target image refers to an image captured by a movable camera, with a smaller field of view but higher resolution, used to obtain detailed information about the QR code target for identification. The control signal is an instruction sent by the processor to the movable camera to drive it to adjust the shooting angle; its specific form can be determined based on the interface type of the movable camera.

[0044] The processor refers to a hardware chip with data processing capabilities, such as a CPU, GPU, SoC, or MCU. In this embodiment, the processor can be a standalone physical chip; when the system is applied to a vehicle, the processor can be a central processing unit integrated into the in-vehicle infotainment system (i.e., the "vehicle infotainment system"). Specifically, in actual product implementation, the processor typically serves as a core component of the vehicle infotainment system, working in conjunction with the system's memory, display screen, speakers, voice interaction module, etc. For example, after executing target detection and decoding algorithms, the processor can output guidance information through the vehicle infotainment system's display screen or speakers, and complete payment authentication through the system's facial recognition module. Those skilled in the art will understand that configuring the processor inside the vehicle infotainment system is a typical implementation of this application, but any in-vehicle computing device containing the processor described in this application falls within the protection scope of this application.

[0045] In some embodiments, the processor is configured to perform the following operations: First, detect a QR code target in an environmental image captured by a wide-angle camera. For example, when the system is applied to a parking lot exit, the environmental image may contain a toll gate in the distance and a QR code posted nearby. The processor locates the QR code target in the environmental image using an image recognition algorithm (such as a deep learning-based detection model).

[0046] Subsequently, based on the position of the QR code target in the environmental image, the processor generates and sends a control signal to the movable camera. This control signal drives the movable camera to adjust its shooting angle so that the QR code target can enter the movable camera's field of view. For example, if the QR code target is located in the upper left corner of the environmental image, the processor can calculate the horizontal and vertical angles that the movable camera needs to rotate based on its pixel coordinates, and generate corresponding control signals to drive the movable camera to rotate in the direction aligned with the QR code target.

[0047] Next, after the movable camera is adjusted, it captures a target image containing the QR code. Due to the small field of view of the movable camera, the QR code in the target image will occupy a larger proportion of pixels, resulting in clearer details. The processor obtains the recognition information of the QR code from this target image. Obtaining the recognition information includes obtaining the result through any decoding method (such as a general library, a dedicated algorithm, OCR, etc.). The recognition information refers to the data carried by the QR code, such as text or link information. In some embodiments, the recognition information may be a payment link, order number, amount, or other data used to complete the payment process carried by the QR code. Finally, the processor triggers the payment process based on the acquired recognition information, for example, by calling a payment interface to complete the deduction.

[0048] In some embodiments of this specification, the system is mounted on a mobile carrier.

[0049] A mobile carrier refers to any platform capable of carrying this system and moving in location. By deploying this system on a mobile carrier, the system can automatically identify QR code targets and complete the payment process whether the carrier is in motion or stationary.

[0050] In some embodiments, the mobile carrier can be a vehicle, such as a private car, taxi, ride-hailing vehicle, or commercial vehicle. Such carriers can be applied to scenarios such as automatic payment at parking lot exits, highway tollbooth payments, and automatic deductions at gas stations. When a vehicle approaches a tollbooth, the system automatically recognizes the distant QR code, obtains payment information, and triggers payment, without requiring the driver to stop or operate a handheld device.

[0051] In other embodiments, the mobile carrier may be a drone, such as a logistics delivery drone. When the drone arrives at the delivery destination or charging station, the system can identify a QR code on the ground or building to complete service fee payment or identity verification.

[0052] In other embodiments, the mobile carrier may be a robot, such as a food delivery robot, an inspection robot, or a service robot. Once the robot arrives at the designated location, it can use the system to scan a QR code provided by the merchant or user to complete order settlement or service confirmation.

[0053] Those skilled in the art will understand that the above examples are for illustrative purposes only and are not intended to limit the invention. Depending on the actual application scenario, those skilled in the art can install this system on any mobile platform that requires automatic long-distance QR code recognition and payment completion, such as ships, automated guided vehicles, electric scooters, etc. Regardless of the specific form of the mobile carrier, as long as the system can move with the carrier and automatically recognize the QR code target at the appropriate location, the technical solutions provided in this specification can be applied.

[0054] In some embodiments of this specification, the mobile carrier is a vehicle. Vehicles, as a common type of mobile carrier, have widespread payment needs in scenarios such as parking lots, toll booths, and gas stations.

[0055] In parking lot exit scenarios, this system, installed on vehicles, can automatically recognize the QR code posted on the gate pillar or toll booth window when a vehicle approaches the gate. The system retrieves a payment link containing parking fees and merchant information from the QR code, automatically deducts the payment, and the gate immediately lifts, allowing the vehicle to exit without stopping.

[0056] In highway toll scenarios, for vehicles that have not installed ETC or whose ETC is temporarily invalid, this system can automatically recognize the dynamic QR code on the toll window and complete the toll payment while driving in mixed lanes or self-service payment lanes.

[0057] In a gas station setting, after a vehicle stops at a refueling station, this system can automatically recognize the QR code on the fuel dispenser or in a convenience store to complete fuel payment or redeem membership points in advance, simplifying the refueling process.

[0058] Based on some embodiments of this specification, by installing the system on a mobile carrier, especially a vehicle, it can automatically complete QR code recognition and payment operations during the carrier's movement, reducing or eliminating the need for manual operation by the user. When the system is combined with features such as dynamic tracking, optical zoom, and super-resolution reconstruction, it can maintain a high recognition success rate under complex conditions such as carrier movement, changes in target distance, and changes in ambient lighting, providing users with a smoother automatic payment experience.

[0059] In a typical application scenario, this system can be installed on a vehicle for automatic payment at parking lot exits. As the vehicle approaches the exit gate, a wide-angle camera captures a real-time environmental image including the gate area. The processor detects a QR code target affixed near the gate from this environmental image. Based on the QR code's position in the environmental image, the processor calculates the angle the movable camera needs to rotate and sends a control signal to align the camera with the QR code. The movable camera then captures a clear image of the QR code target, from which the processor parses a payment link containing the payment amount and merchant identifier, and automatically completes the payment based on this link. The entire process requires no driver to hold a mobile phone, and the vehicle can smoothly drive out of the exit.

[0060] Based on some embodiments of this specification, through the collaborative work of a wide-angle camera and a movable camera, the system can automatically search for and lock onto QR code targets over a large area. Then, by adjusting the viewing angle of the movable camera, it acquires high-definition target images for recognition, realizing a complete process from wide-area search to accurate recognition. This design allows the system to adapt to complex scenarios such as QR code targets being in various positions, at long distances, or in motion, improving the automation level and success rate of QR code recognition. When applied to mobile platforms, the system can reduce or eliminate manual operations by users, improving user experience and operational efficiency. When integrated with payment processes, the system can provide users with a more convenient and seamless automatic payment experience.

[0061] In one or more embodiments of this specification, the processor generates a control signal based on the pixel coordinates of the target in the wide-angle image, and the specific form of the control signal can be flexibly selected according to the interface type of the movable camera.

[0062] In some embodiments, the processor is configured to generate the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; converting the pixel coordinates into a desired attitude angle based on pre-stored camera calibration parameters; generating a control signal containing the desired attitude angle; and the movable camera is configured to rotate to the desired attitude angle in response to the control signal.

[0063] In this embodiment, the movable camera receives angle commands. The processor calculates the desired attitude angle based on the pixel coordinates and camera calibration parameters, generates a control signal containing that angle information, and sends it to the movable camera to drive it to rotate to the target position.

[0064] In other embodiments, the processor is configured to generate the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; generating a control signal containing the pixel coordinates; and the movable camera is configured to: convert the pixel coordinates into a rotation angle and perform rotation based on the rotation angle.

[0065] In this embodiment, the movable camera receives pixel coordinate commands and has a built-in coordinate-to-angle conversion function. The processor directly encapsulates the pixel coordinates as control signals and sends them, while the movable camera internally performs the coordinate-to-angle conversion and executes the rotation. This approach reduces the processor's computational burden and improves the system's modularity.

[0066] In some other embodiments, the processor is configured to generate the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; generating angular velocity information based on the positional deviation between the pixel coordinates and the current field of view center; and generating a control signal containing the angular velocity information.

[0067] In this embodiment, the movable camera receives speed commands. The processor generates angular velocity information based on the deviation between the pixel coordinates and the center of the field of view, and sends it as a control signal.

[0068] In some other embodiments, the processor is configured to generate the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; generating a direction command based on a comparison of the pixel coordinates with a preset threshold; and generating a control signal containing the direction command.

[0069] In this embodiment, the movable camera receives directional commands. The processor generates directional commands such as UP / DOWN / LEFT / RIGHT based on the pixel coordinates relative to a preset threshold and sends them as control signals.

[0070] Those skilled in the art will understand that regardless of the control interface used by the movable camera, as long as it adjusts its viewing angle in response to the control signal generated by the processor based on the target position, so that the target enters its field of view, it falls within the protection scope of this application.

[0071] In some embodiments of this specification, the movable camera has optical zoom capability. Optical zoom refers to adjusting the focal length by changing the relative positions of the lens elements inside the lens, thereby changing the magnification of the image. Unlike digital zoom, optical zoom can bring distant targets closer and magnify them without sacrificing image resolution, obtaining clearer details.

[0072] In some embodiments, the processor is further configured to: in response to determining that the QR code target has entered the field of view of the movable camera, trigger the movable camera to perform an optical zoom operation to improve the resolution of the target image captured by the movable camera. Through the optical zoom operation, the optical image of the QR code target can be magnified to a recognizable size, for example, magnifying the QR code target to a preset magnification, so that the QR code module occupies a sufficient number of pixels in the image, facilitating subsequent decoding and recognition.

[0073] In a specific example, a wide-angle camera first detects a QR code target at a relatively far distance (e.g., 10 meters). The processor drives a movable camera to aim at the QR code target, bringing it into the movable camera's field of view. At this point, the QR code target may only occupy a small pixel area (e.g., 100×100 pixels) in the target image, which is insufficient for reliable decoding. The processor then triggers an optical zoom operation, increasing the focal length to magnify the QR code target in the image to a preset magnification (e.g., 300×300 pixels), thereby obtaining a sufficiently clear image for recognition.

[0074] In other embodiments, the movable camera itself can be a fixed-focal-length telephoto camera with a field of view smaller than that of a wide-angle camera, for example, 1 / 5 to 1 / 10 of the wide-angle camera's field of view. In this configuration, the wide-angle camera is responsible for a wide-range search, while the fixed telephoto camera provides a natural optical zoom effect—due to its smaller field of view, distant QR code targets naturally occupy a larger proportion of pixels in the telephoto camera image. This implementation method has a relatively simple structure and is suitable for application scenarios where the target distance is relatively fixed.

[0075] In practical applications, the system can be installed on a mobile carrier (such as a vehicle), and the mobile carrier may be stationary. For example, after the vehicle has come to a complete stop, the system performs the zoom operation described above to acquire a high-definition image. In a stationary state, there is no relative motion between the target and the camera, and the optical zoom operation can be performed stably to obtain a clear magnified image.

[0076] Based on some embodiments of this specification, by combining optical zoom operation with the determination of target entry into the field of view, the system can automatically magnify the image after initial target alignment, thereby obtaining a high-resolution recognition image while maintaining a wide search range. This design enables the system to adapt to scenarios with large variations in target distance, and can still acquire sufficiently clear QR code images for subsequent recognition even at long distances, improving the system's applicability and recognition success rate.

[0077] In some embodiments of this specification, the processor is further configured to: determine, before triggering the optical zoom operation, that the positional fluctuation of the QR code target in a series of target images captured by the movable camera is less than a preset threshold.

[0078] The phrase "position fluctuation less than a preset threshold" means that the change in pixel coordinates of the target in N consecutive frames of images is less than M pixels, or the jitter amplitude of the target bounding box is less than a certain proportion, indicating that the target is relatively stationary.

[0079] In practical applications, the system can be installed on a mobile carrier, which may be in motion. For example, if a vehicle is slowly approaching an exit gate at a low speed (e.g., 5 km / h), the system needs to ensure that the target is stable in the image before optically zooming in; otherwise, the target may quickly move out of the field of view or produce severe motion blur after zooming.

[0080] In some embodiments, the processor continuously analyzes multiple consecutive frames of target images captured by the movable camera and calculates the change in pixel coordinates of the center point of the QR code target. When the horizontal and vertical displacements of the target center point are both less than 3 pixels in three consecutive frames, the system determines that the target has met the stability condition. At this time, the processor triggers an optical zoom operation to magnify the target to a preset magnification, while continuing to keep the target in the center of the field of view through a dynamic tracking mechanism.

[0081] In another implementation, when the system determines that the target is not yet stable, the processor can output a prompt message through the user interaction module, such as "Please reduce your speed to facilitate scanning," guiding the moving vehicle to slow down and assist in stabilizing the target. This human-computer interaction design helps improve the system's recognition success rate in motion.

[0082] Based on some embodiments of this specification, by introducing a stabilization determination mechanism before triggering optical zoom, the system can select an appropriate time to magnify the image while in motion, avoiding image blurring or target loss due to target movement. This design makes the system suitable not only for static scenes but also for recognition needs when moving vehicles are moving slowly, expanding the system's application range. As the vehicle gradually approaches the target, the system can intelligently select a moment when the target is relatively stable to perform zoom, thus obtaining a clear and identifiable QR code image even while in motion.

[0083] In some embodiments of this specification, the processor is configured to obtain the identification information of the QR code target from the target image captured by the movable camera in the following manner: performing super-resolution reconstruction processing on the target image captured by the movable camera to generate an enhanced image; and parsing the identification information of the QR code target from the enhanced image.

[0084] The super-resolution reconstruction process refers to the process of recovering a high-resolution image from a low-resolution image using algorithms. Specifically, the processor performs registration operations on multiple consecutive frames of target images captured by a movable camera to eliminate inter-frame offsets caused by carrier motion or camera shake. After registration, the processor extracts high-frequency detail information from each frame, which may be scattered across different frames. Subsequently, the processor fuses this information through interpolation, reconstruction, or deep learning-based methods (such as SRCNN, SRGAN, and other network structures) to generate a single high-resolution enhanced image. This enhanced image has clearer edges and richer texture details compared to the original image, better presenting the boundaries and distribution of the QR code module, facilitating subsequent decoding operations.

[0085] In a specific example, a vehicle slowly drives towards the parking lot exit gate. A movable camera, positioned 10 meters away, aims at the QR code target and captures five consecutive frames of the target image. Due to slight vehicle vibrations and road bumps, individual frames may contain motion blur or noise, potentially causing direct decoding failure. The processor registers these five frames, aligning them with the QR code's position, and then uses a super-resolution reconstruction algorithm to extract and fuse the clearer portions from each frame. For example, the upper left corner of the QR code is clear in the first frame, and the lower right corner is clear in the second frame. The fused enhanced image integrates the advantages of each frame, significantly improving overall clarity. The processor then decodes this enhanced image, successfully obtaining the payment link.

[0086] In some embodiments, super-resolution reconstruction processing can be combined with optical zoom. For example, the processor first triggers optical zoom to enlarge the QR code target to a larger size. If the image is still blurry due to insufficient light or motion, super-resolution reconstruction processing is then performed on multiple consecutive frames of the zoomed image to further improve image quality. The combination of the two can achieve better recognition results under conditions of long distance, low light, or motion.

[0087] Based on some embodiments of this specification, by performing super-resolution reconstruction processing on the target image captured by the movable camera, the system can recover a clearer enhanced image even under conditions of limited image quality (such as insufficient resolution due to long-distance shooting, blurring due to carrier movement, and noise due to changes in lighting), thereby improving the success rate of QR code decoding. This processing method allows the system to maintain high recognition reliability even under adverse conditions. Compared with the implementation method of directly decoding the original image, this embodiment provides a higher quality input image to the decoder by introducing an image enhancement step before decoding, which helps to expand the applicability of the system in complex environments. Those skilled in the art will understand that the implementation method of enhancing the original image first and then decoding from the enhanced image, and the implementation method of directly decoding the original image, constitute parallel technical paths, which can be selected and used according to the needs of the actual application scenario.

[0088] In some embodiments of this specification, the system is mounted on a mobile carrier, and the processor is further configured to perform dynamic tracking compensation operations during the movement of the mobile carrier. The processor is also configured to: acquire position change information of the QR code target in multiple consecutive frames of environmental images captured by the wide-angle camera during the movement of the mobile carrier; generate dynamic compensation instructions based on the position change information; and drive the movable camera to perform reverse motion compensation based on the dynamic compensation instructions, so that the QR code target remains stable within the field of view of the movable camera.

[0089] Dynamic tracking compensation refers to the process by which the system, during the movement of the mobile carrier, analyzes the target's positional changes in the image in real time and drives the movable camera to move in the opposite direction to counteract the relative displacement caused by the carrier's movement, thereby keeping the target stable within the movable camera's field of view. The positional change information refers to the change in pixel coordinates of the QR code target in multiple consecutive frames of environmental images captured by the wide-angle camera, reflecting the relative motion relationship between the target and the carrier. The dynamic compensation instruction is a signal generated by the processor based on the positional change information and used to control the movement of the movable camera.

[0090] In some embodiments, the processor is configured to generate dynamic compensation instructions based on a PID control algorithm. The input to the PID control algorithm is the positional offset of the QR code target between the current frame and the previous frame, such as the horizontal pixel difference Δx and the vertical pixel difference Δy. The algorithm performs proportional (P), integral (I), and derivative (D) operations on these offsets, outputting an angular velocity control signal for the movable camera. This signal drives the gimbal motor to rotate at a corresponding speed, thereby continuously counteracting the target's drift in the image. Through closed-loop adjustment of the PID algorithm, the system can achieve smooth tracking of moving targets, reducing overshoot and steady-state error.

[0091] In some embodiments, the processor is further configured to: acquire motion sensor data of the mobile carrier, including at least one of inertial measurement unit data (such as acceleration, angular velocity), wheel speed pulses, and heading angle data. The processor fuses this motion sensor data with position change information of the QR code target in a wide-angle image to predict the position of the QR code target at a future time. Based on the predicted position, the processor generates a feedforward compensation command to drive the movable camera to perform compensating motion in advance. This feedforward control method, which integrates sensor data, can compensate for tracking lag caused by system delay to a certain extent, improving the timeliness and accuracy of dynamic compensation.

[0092] In some embodiments, the system further includes a user interaction module. When the processor detects that the target is moving too fast and is difficult to track stably, the user interaction module can generate and output guidance information to prompt the moving vehicle to reduce its speed before triggering the optical zoom operation. For example, by saying "Please reduce your speed to scan the code" in voice or displaying a deceleration prompt on the screen, the driver is guided to actively slow down, assisting the system in completing the target locking and zoom operations.

[0093] Based on some embodiments of this specification, by acquiring target position change information in real time during the movement of the mobile carrier and driving the movable camera to perform reverse motion compensation, the system can effectively counteract the relative displacement caused by the carrier's movement, keeping the QR code target stable within the field of view of the movable camera. This dynamic tracking mechanism creates stable conditions for subsequent optical zoom and image recognition. When a PID control algorithm is introduced, the system can achieve smooth and continuous tracking of moving targets, reducing target loss caused by sudden movements. When motion sensor data is fused to generate feedforward compensation commands, the system can predict the target's movement trend to a certain extent, perform compensation in advance, and further improve tracking accuracy and response speed. Combined with the vehicle speed guidance function of the user interaction module, the system can proactively prompt deceleration when the target moves too fast, assisting in achieving stable locking. These features work together to enable the system to maintain stable observation of the QR code target even when the mobile carrier is in motion, laying the foundation for subsequent high-definition imaging and recognition.

[0094] In one or more embodiments of this specification, before acquiring target recognition information from images captured by a movable camera, the processor first determines whether the target meets preset recognition conditions. If the conditions are met, recognition is performed directly; if not, guidance information is generated to instruct the moving vehicle to approach the target.

[0095] Specifically, the processor is further configured to: determine whether the QR code target meets preset recognition conditions based on the pixel size or module pixel density of the QR code target in the target image; if the preset recognition conditions are not met, generate distance guidance information to instruct the mobile carrier to move towards the QR code target. If the preset recognition conditions are met, obtain the recognition information carried by the QR code from the target image. Here, the module pixel density refers to the average number of pixels occupied by each module in the QR code in the image.

[0096] In one implementation, the processor determines whether the recognition conditions are met based on pixel size by: obtaining the total number of pixels (e.g., width × height) occupied by the QR code in the target image; and comparing the total number of pixels with a preset first threshold. If the total number of pixels is less than the first threshold, the QR code is determined to be too small and cannot guarantee a successful decoding rate, i.e., the recognition conditions are not met, and guidance information needs to be generated; if the total number of pixels is greater than or equal to the first threshold, the recognition conditions are determined to be met, and decoding can be performed directly.

[0097] In another implementation, the processor determines whether the recognition conditions are met based on the module pixel density by: identifying the module layout in the QR code and obtaining the average number of pixels occupied by each module in the image; comparing the average number of pixels with a preset second threshold. If the average number of pixels is less than the second threshold, it is determined that each module is too small and difficult to sample accurately, i.e., the recognition conditions are not met, and guidance information needs to be generated; if the average number of pixels is greater than or equal to the second threshold, it is determined that the recognition conditions are met, and decoding can be performed directly. In practical applications, the module pixel density reflects the true recognizability of the QR code better than the pixel size, because different versions of QR codes have different total numbers of modules, and the module pixel density directly corresponds to the sampling accuracy requirements of the decoding algorithm for each module.

[0098] In one or more embodiments of this specification, the timing for determining whether a target meets the recognition criteria can be flexibly chosen. In one implementation, the processor performs a preliminary judgment before attempting decoding; if the target is too small, it directly guides the processor to avoid invalid decoding attempts. In another implementation, the processor first attempts decoding; if decoding fails, it then determines whether the failure is due to the target being too small and outputs guidance information. The above two timing methods can be used individually or in combination; for example, a preliminary judgment can be made before decoding, and if decoding fails, a post-judgment mechanism can be used to confirm the cause.

[0099] Those skilled in the art will understand that the two judgment timings described above can be used individually or in combination. For example, the system can first execute a pre-judgment strategy; if the recognition conditions are met, it can directly decode; if decoding fails, it can then execute a post-judgment strategy to determine whether the failure was due to a distance issue and output guidance information.

[0100] In one or more embodiments of this specification, the system further includes a user interaction module communicatively connected to the processor, configured to output the distance guidance information to a user.

[0101] In some embodiments, the user interaction module includes at least one of the following: a voice interaction module for outputting the distance guidance information in the form of voice broadcast (such as "Please drive forward 2 meters"); a display screen for displaying the distance guidance information (such as guide arrows, distance indicators) in the form of a visual interface; and a head-up display for projecting the guidance information in front of the driver's line of sight to ensure that the driver can obtain instructions without looking down.

[0102] Furthermore, after generating and outputting the distance guidance information, the processor continues to monitor the module pixel density of the QR code target in the wide-angle camera or the target image. In response to the module pixel density increasing to exceed a preset threshold (i.e., meeting the recognition conditions), the processor automatically re-executes the steps of sending control signals to the movable camera to drive it to adjust its viewing angle, and the steps of obtaining the recognition information of the QR code target from the target image captured by the movable camera, without requiring the user to trigger it again.

[0103] In the context of vehicle applications, the user interaction module is specifically a vehicle-machine interaction module. The vehicle-machine interaction module is communicatively connected to the processor and is configured to: receive guidance information generated by the processor; and / or output voice guidance prompts through the voice interaction module; and / or display a visual guidance interface through the display screen.

[0104] In one or more embodiments of this specification, the processor is configured to detect a QR code target from an environmental image captured by the wide-angle camera by: acquiring positioning data, heading angle data, and map data of the mobile carrier; predicting a candidate region in the environmental image where the QR code target may appear based on the positioning data, heading angle data, and map data; and performing target detection within the candidate region.

[0105] In this context, the candidate region refers to the range of locations in the environmental image where the QR code target is most likely to appear, predicted based on prior information. Unlike performing a global search across the entire image, performing target detection within the candidate region significantly reduces computational load and improves detection efficiency. Location data refers to the current location information of the mobile vehicle, such as GPS coordinates. Heading angle data refers to the orientation information of the mobile vehicle. Map data refers to prior geographic information data related to the current environment of the mobile vehicle, containing the possible installation location of the QR code target. Map data is used to assist the system in predicting candidate regions where the QR code target will appear in the environmental image, narrowing the search range for target detection. The map data can be any data that can provide prior information about the possible location of the target, including but not limited to building information models, 3D point clouds of the scene, facility distribution maps, and even users' historical scanning location records, all of which can be considered as the map data described in the embodiments of this specification.

[0106] Specifically, the processor is configured to detect QR code targets from environmental images captured by a wide-angle camera in the following manner: First, it acquires the location data, heading angle data, and map data of the mobile vehicle. This data can be obtained from onboard sensors (such as GPS modules and IMUs) and a pre-stored map database. Then, based on the location data, heading angle data, and map data, the processor predicts candidate regions in the environmental image where the QR code target may appear. Specifically, the processor can determine the current position and orientation of the mobile vehicle based on the location data and heading angle data, and then, combined with the possible installation locations of the QR code marked in the map data (such as gate pillars and toll booth windows), estimate the corresponding pixel areas in the wide-angle image through geometric calculations, and identify these areas as candidate regions. Finally, the processor performs target detection within the candidate regions, rather than performing a global search across the entire environmental image.

[0107] In one implementation, the processor first generates a probability distribution map of the QR code target in the environmental image based on positioning data, heading angle data, and map data. This probability distribution map reflects the likelihood of the QR code appearing in different image locations. Then, the processor determines the detection area of ​​the QR code target in the environmental image based on the probability distribution map, for example, selecting areas with probability values ​​greater than a preset threshold as the detection area.

[0108] For example, when a vehicle enters the parking lot exit area, the processor acquires the vehicle's GPS positioning data, IMU heading angle data, and pre-stored high-precision map data of the parking lot. Based on the vehicle's current position and heading, combined with the marking information of facilities such as turnstiles and toll booths on the map, the processor determines that the turnstile is located to the right front of the vehicle. Therefore, it predicts approximately one-third of the right side of the wide-angle image as a candidate area where a QR code may appear. Subsequent object detection algorithms will primarily search within this area.

[0109] In one or more embodiments of this specification, the processor is configured to perform object detection within the candidate region by employing a deep learning-based object detection model to detect QR code targets within the candidate region; wherein the QR code target detection model is trained based on QR code sample data and is used to identify QR code patterns in the input image. Specifically, the object detection model is trained by collecting a large amount of QR code sample data and is capable of accurately identifying QR code patterns in the image and outputting their location information.

[0110] In one implementation, the object detection model employs the YOLOv5 architecture. This model is trained using a large amount of QR code sample data collected in parking lot scenarios, exhibiting lightweight, fast, and accurate characteristics, making it suitable for real-time detection requirements in in-vehicle environments. After receiving the input image, the model performs forward computation within the candidate region, outputting the bounding box coordinates of the QR code in the image. This coordinate information will be used in subsequent gimbal control and image acquisition steps.

[0111] In some embodiments, the processor is further configured to expand the search range to the entire wide-angle camera image and re-perform target detection when no target is detected within the candidate area. For example, if inaccurate map data or temporary changes in the QR code location result in no QR code target being found within the predicted candidate area, the processor can automatically expand the search range to the entire wide-angle image and may employ traditional methods such as sliding windows to assist detection. This fault-tolerant mechanism helps improve the robustness of the system, ensuring that the target detection task can still be completed even when predictions are inaccurate.

[0112] In a specific application scenario, the system installed on a vehicle enters the parking lot exit area. The processor acquires the vehicle's GPS positioning data (showing the vehicle is approximately 15 meters from the exit), IMU heading angle data (showing the vehicle is facing due north), and parking lot map data (showing the exit gate is located on the right side of the lane). Based on this information, the processor predicts that the QR code on the gate should appear in the right-hand area of ​​the wide-angle image, thus setting the right 1 / 3 of the image as a candidate region. Subsequently, the processor inputs the candidate region image into a trained YOLOv5 object detection model. The model detects a QR code pattern within this region and outputs its bounding box coordinates. The processor uses this coordinate information for subsequent gimbal control steps. If no QR code is detected within this candidate region, the processor automatically expands the search range to the entire wide-angle image and re-executes the detection, ensuring the system can handle possible prediction biases.

[0113] Based on some embodiments of this specification, by acquiring the positioning data, heading angle data, and map data of the mobile vehicle, candidate regions for QR code targets in environmental images are predicted, and target detection is performed within these candidate regions. This significantly narrows the search range and reduces unnecessary computational overhead. When a deep learning-based detection model is introduced and fine-grained detection is performed within the candidate regions, the system can achieve faster detection speeds while maintaining detection accuracy, meeting the real-time requirements of in-vehicle environments. The combination of candidate region prediction and deep learning detection allows the system to concentrate limited computational resources on the locations where targets are most likely to appear, improving overall detection efficiency. If no target is detected within the candidate regions, the search range is automatically expanded, enabling the system to handle situations of inaccurate predictions or changes in target location, enhancing the system's robustness and adaptability. This coarse-to-fine, prediction-to-detect target detection strategy lays a reliable foundation for subsequent gimbal alignment and high-definition imaging.

[0114] In one or more embodiments of this specification, the processor is further configured to: in response to detecting that the mobile carrier has entered a preset geofence, or receiving a trigger command from a user via a user interaction module, activate the system to begin executing the step of detecting a QR code target in an environmental image captured by the wide-angle camera.

[0115] Specifically, system activation can be achieved through either automatic or manual triggering.

[0116] In some embodiments employing an automatic triggering method, the system determines whether a mobile vehicle has entered a preset area based on geofencing technology. In one specific embodiment, the geofencing is determined based on the presence of a mobile vehicle detected by a ground-inductive loop installed in front of the gate; that is, the sensing range of the ground-inductive loop constitutes the geofencing. Specifically, a ground-inductive loop is buried underground in front of the parking lot exit gate. When a vehicle passes the loop, the loop detects the vehicle's presence and generates a trigger signal. The vehicle-mounted system can receive this signal via Dedicated Short Range Communication (DSRC), On-Board Unit (OBU), or any vehicle-to-infrastructure communication method known in the art, or obtain trigger information through wireless communication between the vehicle and the parking system, thereby activating the identification process. After system activation, the processor can output prompt information through the user interaction module to inform the user that the system has entered identification mode, such as through a voice prompt "Scanning payment QR code" or by displaying the scanning interface on a screen, to improve the user experience.

[0117] In some embodiments employing a manual triggering method, the system further includes a user interaction module communicatively connected to the processor. This user interaction module may include a touch interface, a voice interaction module, physical buttons, etc. The trigger command is transmitted through the user interaction module. For example, a user can send a trigger command to the system by clicking the "Pay" icon on a touch interface (such as a vehicle infotainment system), saying "I want to pay" via voice command, or pressing a dedicated physical button inside the vehicle. Upon receiving the command, the system immediately activates and begins QR code detection and recognition.

[0118] Those skilled in the art will understand that the two triggering methods described above can be used individually or in combination. For example, geofencing-based automatic triggering can be the primary method, with manual triggering as a supplementary or backup solution. In the automatic triggering method, if the system fails to activate due to communication failures or other reasons, the user can initiate the identification process at any time via manual triggering, ensuring the robustness of the system. Regardless of the triggering method used, as long as the system responds to the triggering conditions and begins executing the target detection steps, it falls within the protection scope of this solution.

[0119] In one or more embodiments of this specification, the processor is further configured to: in response to successfully acquiring the identification information of the QR code target from the target image, capture a user's facial image through an in-vehicle camera; perform identity recognition on the user based on the facial image; and trigger a payment process in response to successful identity recognition.

[0120] The term "in-vehicle camera" refers to a camera device installed inside a mobile vehicle to capture images of users' faces. When the mobile vehicle is a vehicle, this camera could be a driver monitoring camera near the rearview mirror, a camera on the dashboard, or other devices used to capture facial images of occupants. Identity verification refers to the process of comparing the captured facial image with a pre-stored facial template, or verifying identity through a cloud-based payment platform, to confirm the user's identity. The payment process refers to the process of sending a deduction request to the payment platform and completing the transaction based on payment-related information obtained from the QR code.

[0121] In practical applications, the identification information that can be obtained from a QR code may include, but is not limited to, one or more of the following: payment link (URL), payment amount, merchant identifier, order number, etc. The processor can directly call the payment interface to complete the deduction based on the content of the identification information, or redirect to the payment page for subsequent operations. After successfully obtaining the QR code identification information from the image captured by the movable camera, the system uses the in-vehicle camera to perform facial recognition to trigger the payment process, ensuring payment security and user experience.

[0122] In one implementation, in response to successfully acquiring identification information (e.g., a payment link containing the payment amount and merchant identifier), the processor immediately activates the in-vehicle camera to capture the user's facial image. For example, when a vehicle approaches a parking lot exit gate, after the system successfully recognizes the QR code and parses the payment information, the in-vehicle camera immediately captures the driver's facial image. The processor compares the captured facial image with a pre-stored user facial template or uploads it to a cloud-based payment platform for identity verification. In response to successful identity recognition, the processor automatically triggers the payment process, sending a deduction request to the payment platform. The entire process requires no additional user intervention, providing a smooth and automated payment experience.

[0123] In an optional embodiment, the processor is further configured to automatically complete the payment process in response to successful identity verification, without requiring additional user confirmation. This method is suitable for scenarios where high payment convenience is required, such as parking lot exits where passage is quick.

[0124] In optional embodiments, to meet the payment habits of different users or specific security policy requirements, the processor is further configured to: output a payment confirmation prompt through the user interaction module in response to successful identity verification; and trigger the payment process in response to receiving a confirmation command input by the user through the user interaction module. Specifically, after successful identity verification, the processor outputs a payment confirmation prompt through the user interaction module (such as an in-vehicle display screen), displays the payment amount and payment recipient, and prompts the user for confirmation. In response to a confirmation command input by the user via a touchscreen, voice command, or physical button, the processor then triggers the payment process. This method, while ensuring security, gives the user ultimate control and is suitable for scenarios with explicit requirements for payment confirmation.

[0125] In some embodiments, the processor is further configured to: in response to identity recognition failure, output a prompt message through the user interaction module to instruct the user to pay using an alternative payment method. Specifically, if face recognition fails (e.g., due to dim lighting, obscured face, or the user not having pre-registered their face information), the processor outputs a prompt message through the user interaction module, such as "Face recognition failed, please use your mobile phone to scan the QR code to pay," guiding the user to complete the payment using an alternative payment method, ensuring that the system can complete the payment process under any circumstances.

[0126] In some embodiments, the processor is further configured to: after triggering the payment process, output a payment result prompt message through the user interaction module. After the payment process is completed, the processor outputs a payment result prompt message through the user interaction module, such as a voice announcement saying "Payment successful, have a safe journey," or displays a payment success interface on the vehicle's infotainment display screen, thereby improving the user experience.

[0127] In a specific application scenario, as a vehicle enters the parking lot exit area, the system, through the collaborative work of a wide-angle camera and a movable camera, successfully identifies the QR code posted on the gate and extracts a payment link containing the payment amount "10 yuan" and the merchant's logo. The processor then activates the in-vehicle camera to capture the driver's facial image. This image is successfully compared with the pre-stored facial templates in the system, and identity verification is successful. According to the system's preset payment confirmation method, if set to automatic payment, the processor directly sends a deduction request to the payment platform to complete the payment; if set to require confirmation, the vehicle's display screen shows "Amount due: 10 yuan, please confirm payment." After the driver confirms payment via voice command, the processor triggers the payment process. Upon successful payment, the system announces "Payment successful, have a safe journey." If facial recognition fails (e.g., the driver wearing sunglasses causes recognition failure), the system announces "Facial recognition failed, please use your mobile phone to scan the code to pay," guiding the user to complete the payment via their mobile phone.

[0128] Based on some embodiments of this specification, by introducing a facial recognition step after successfully acquiring QR code recognition information, the system can verify the user's identity before payment, adding a layer of security to the payment process. Automatic payment after successful facial recognition reduces user operations and provides a smoother payment experience; while payment methods requiring confirmation give the user final control while ensuring security. When identity recognition fails, the system can promptly guide the user to use an alternative payment method, avoiding interruption of the payment process due to recognition failure. The result prompt after payment completion helps users understand the payment status in a timely manner, improving the user experience. The facial recognition payment mechanism, combined with features such as QR code recognition, dynamic tracking, and optical zoom, constitutes a complete process from target detection to secure payment, providing users with an automatic payment solution that combines security and convenience in different scenarios.

[0129] The various technical features in the above embodiments can be combined arbitrarily, as long as there is no conflict or contradiction between the combinations of features. However, due to space limitations, they have not been described one by one. Therefore, the arbitrary combination of various technical features in the above embodiments is also within the scope of this specification.

[0130] Based on some embodiments of this specification, through the collaborative work of a wide-angle camera and a movable camera, the system can automatically search for and lock onto QR code targets over a large area. Then, by adjusting the viewing angle of the movable camera, it acquires high-definition target images for recognition, realizing a complete process from wide-area search to accurate recognition. This dual-camera linkage architecture allows the system to begin the recognition process at a distance of 5-15 meters, and even at long distances, it can still obtain sufficiently clear QR code images, exhibiting a long effective recognition distance and a high recognition success rate.

[0131] Based on some embodiments of this specification, by introducing a dynamic tracking compensation mechanism, the system can analyze the target's positional changes in the image in real time during the movement of the mobile carrier, and drive the movable camera to perform reverse motion compensation, keeping the target stable within the field of view of the movable camera. Combined with optical zoom operation and super-resolution reconstruction processing, the system can obtain clear enhanced images in complex environments such as shaking and low light, effectively improving the success rate of QR code recognition. Through sensor fusion and candidate region prediction, the system can adapt to various complex QR code installation locations, exhibiting strong environmental adaptability.

[0132] Based on some embodiments of this specification, after successfully recognizing a QR code, the system can acquire a user's facial image via an in-vehicle camera for identity verification, and trigger a payment process based on the acquired payment information, forming a complete closed loop from target detection to secure payment. As the user approaches, the system automatically completes all recognition and payment steps without manual operation, providing a smooth and seamless payment experience. In scenarios such as parking lots and toll stations, the processing time per vehicle is reduced, helping to alleviate congestion at entrances and exits and improve overall traffic efficiency.

[0133] In one or more embodiments of this specification, a QR code-based payment system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The payment-related information of the QR code target is obtained from the target image captured by the movable camera; The payment process is triggered based on the payment-related information.

[0134] Referring to the descriptions of "environmental image," "target image," "control signal," and "QR code target" above, in some embodiments of this specification, "payment-related information" refers to data parsed from the QR code that can be used to complete a payment transaction, including but not limited to payment link (URL), merchant identifier, order number, payment amount, and receiving account. This information typically conforms to a specific encoding format, such as a payment gateway link starting with "https: / / " or a merchant order number containing a specific prefix.

[0135] Referring to the descriptions above regarding "detecting the QR code target," "sending control signals," and "adjusting the shooting angle," in some embodiments, the processor is further configured to: obtain payment-related information of the QR code target from the target image captured by the movable camera, and trigger a payment process based on the payment-related information.

[0136] Specifically, once the movable camera successfully aligns with the QR code target and captures a clear image, the processor decodes the image and extracts the payment-related information it contains. Depending on the content of this information, the processor can employ different payment triggering methods. For example, if the payment-related information is a payment link containing the payment amount and merchant identifier, the processor can directly call the payment interface to send a deduction request to that link; if the payment-related information is a redirect page link, the processor can display the payment page on the in-vehicle display screen for user confirmation to complete the payment.

[0137] In some embodiments, the system is mounted on a mobile carrier, including a vehicle.

[0138] Referring to the description of "mobile carrier" above, in some embodiments of this specification, the system is installed on a mobile carrier, including a vehicle. For example, the system can be installed on various vehicles such as private cars, taxis, and ride-hailing vehicles, and applied to scenarios such as parking lot exit payments, highway toll station payments, and automatic deductions at gas stations. When the vehicle approaches the toll point, the system automatically identifies the distant QR code target, obtains payment-related information, and triggers the payment process, without requiring the driver to stop or operate a handheld device.

[0139] In a specific example, a vehicle enters the parking lot exit area. A wide-angle camera detects a QR code affixed to the gate in the environmental image. The processor, based on its position, drives a movable camera to align with the QR code and capture a target image. From this target image, the processor parses the payment link. The processor then sends a deduction request to the payment link via the vehicle communication module, completing the automatic payment of the parking fee, and the gate opens to allow passage. The entire process requires no driver intervention.

[0140] Based on some embodiments of this specification, by combining a dual-camera linkage architecture with the payment process, the system can automatically obtain the payment-related information carried by the QR code target and trigger payment after detection, forming a complete closed loop from target detection to payment completion. This design allows users to complete payments without manual operation, providing a convenient automatic payment experience in scenarios such as parking lots, toll booths, and gas stations, helping to reduce the time vehicles spend at toll points and improve traffic efficiency.

[0141] In one or more embodiments of this specification, a target recognition system is provided, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; and The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust its shooting angle so that the QR code target enters the field of view of the movable camera; and The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger business processing.

[0142] Referring to the description above regarding the connection relationship between the wide-angle camera, the movable camera, and the processor, one or more embodiments of this specification provide a target recognition system, including a wide-angle camera, a movable camera, and a processor. The wide-angle camera is used to acquire environmental images and has a large field of view, capable of covering a wide area. The movable camera has a smaller field of view than the wide-angle camera and has the ability to adjust the shooting angle. The processor is communicatively connected to the wide-angle camera and the movable camera, used to control their operation and process the acquired image data.

[0143] In some embodiments, the processor is configured to perform the following operations: First, detect a QR code target in an environmental image captured by a wide-angle camera. Then, based on the position of the QR code target in the environmental image, send a control signal to a movable camera to drive the movable camera to adjust its shooting angle so that the QR code target enters the field of view of the movable camera. Finally, obtain the QR code target's identification information from the target image captured by the movable camera; this identification information is used to trigger business processing.

[0144] It should be noted that although some embodiments in this specification are described using payment processes as examples, the technical solutions of the present invention are not limited thereto. Those skilled in the art will understand that the dual-camera linkage recognition system provided by the present invention can be applied to any scenario requiring automatic long-distance alignment with QR codes and acquisition of recognition information, such as member registration, event check-in, information query, and access control.

[0145] In one specific application scenario, this system can be installed on vehicles for automatic payment at parking lot exits. As a vehicle approaches the exit gate, a wide-angle camera captures an environmental image including the gate area. The processor detects a QR code target affixed to the gate pillar from this image. Based on the QR code's position in the image, the processor calculates the required rotation angle of the movable camera and sends a control signal to align the camera with the QR code. The camera then captures a clear image of the QR code target, from which the processor parses a payment link containing the payment amount and merchant identifier, triggering the payment process based on this link.

[0146] In another example, the system can be used for automated check-in at events. Participants approach the check-in gate holding an admission ticket containing the event's QR code. The system automatically detects and recognizes the QR code, extracting the participant's identity information and check-in status, and triggering a check-in completion notification. The entire process requires no manual scanning by the participant.

[0147] In another example, the system can be installed at the entrance of a shopping mall for membership registration. When a customer approaches the registration terminal, the system automatically recognizes the registration QR code displayed on the customer's mobile phone, obtains their membership information, and automatically fills in the registration form, simplifying the registration process.

[0148] Based on some embodiments of this specification, through the collaborative work of a wide-angle camera and a movable camera, the system can automatically search for and lock onto QR code targets over a large area. Then, by adjusting the viewing angle of the movable camera, it acquires high-definition images of the targets for recognition, realizing a complete process from wide-area search to accurate recognition. This design allows the system to adapt to complex scenarios such as QR code targets with unpredictable locations and long distances, improving the automation level and success rate of QR code recognition. When the recognized information is used to trigger various business processes, the system can provide users with a more convenient and seamless automated experience, reducing the need for manual operation and improving business processing efficiency.

[0149] Figure 3 This is a flowchart illustrating a target recognition method provided in an embodiment of this specification.

[0150] From a procedural perspective, the entity executing the process can be a program mounted on a terminal device. It can be understood that this method can be executed by any device, equipment, or platform with computing and processing capabilities.

[0151] like Figure 3 As shown, the process may include the following steps: Step 302: Acquire environmental images captured by the wide-angle camera.

[0152] Step 304: Detect the QR code target from the environmental image.

[0153] Step 306: Based on the position of the QR code target in the environmental image, send a control signal to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera.

[0154] Step 308: Obtain the identification information of the QR code target from the target image captured by the movable camera. The identification information is used to trigger the payment process.

[0155] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps and does not represent the only possible execution order. The order of some steps may be adjusted according to actual needs, or some steps may be omitted. When the claims involve method steps, changes in the order of such steps, or parallel execution between steps, are also within the scope of protection of the claims.

[0156] Figure 3 The method described in this paper first uses a wide-angle camera to capture environmental images and detect QR code targets within them. Then, based on the position of the QR code target in the environmental image, a control signal is sent to a movable camera. This drives the movable camera, which has a smaller field of view, to adjust its shooting angle so that the QR code target enters its field of view. Finally, the recognition information of the QR code target is obtained from the target image captured by the movable camera to trigger the payment process. Through the collaborative work of the wide-angle camera and the movable camera, this solution can automatically search for and lock onto QR code targets over a large area. Then, by precisely aligning the movable camera, it acquires high-definition target images for recognition, achieving an automated process from wide-area search to accurate recognition. This allows the system to adapt to complex scenarios where the QR code target's position is not fixed or the distance is far, completing QR code recognition and payment triggering without manual user operation, thus improving operational convenience and recognition success rate.

[0157] based on Figure 3 In addition to the method described herein, this specification also provides some improved implementation methods, which will be described below.

[0158] In some embodiments, the target recognition method further includes generating the control signal by: obtaining the pixel coordinates of the QR code target in the environmental image; converting the pixel coordinates into a desired attitude angle based on pre-stored camera calibration parameters; generating a control signal containing the desired attitude angle; the control signal being used to control the movable camera to rotate to the desired attitude angle.

[0159] In some embodiments, the target recognition method further includes generating the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; generating a control signal containing the pixel coordinates; the control signal being used to instruct the movable camera to convert the pixel coordinates into a rotation angle and perform rotation based on the rotation angle.

[0160] In some embodiments, the movable camera has optical zoom capability; before obtaining the identification information of the QR code target from the target image captured by the movable camera, the method further includes: in response to determining that the QR code target has entered the field of view of the movable camera, triggering the movable camera to perform optical zoom operation to improve the resolution of the target image captured by the movable camera.

[0161] In some embodiments, the target recognition method further includes: determining, before triggering the optical zoom operation, that the positional fluctuation of the QR code target in a series of target images captured by the movable camera is less than a preset threshold.

[0162] In some embodiments, obtaining the identification information of the QR code target from the target image captured by the movable camera specifically includes: performing super-resolution reconstruction processing on the target image captured by the movable camera to generate an enhanced image; and parsing the identification information of the QR code target from the enhanced image.

[0163] In some embodiments, the method is applied to a mobile carrier.

[0164] In some embodiments, the target recognition method further includes: during the movement of the mobile carrier, acquiring position change information of the QR code target in multiple consecutive frames of environmental images captured by the wide-angle camera; generating a dynamic compensation instruction based on the position change information; and driving the movable camera to perform reverse motion compensation based on the dynamic compensation instruction, so that the QR code target remains stable within the field of view of the movable camera.

[0165] In some embodiments, the target recognition method further includes: determining whether the QR code target meets preset recognition conditions based on the pixel size or module pixel density of the QR code target in the target image; if the preset recognition conditions are not met, generating distance guidance information to instruct the mobile carrier to move towards the QR code target.

[0166] In some embodiments, the target recognition method further includes: outputting the distance guidance information to the user through a user interaction module.

[0167] In some embodiments, detecting a QR code target in an environmental image captured by the wide-angle camera specifically includes: acquiring the positioning data, heading angle data, and map data of the mobile carrier; predicting a candidate region in the environmental image where the QR code target may appear based on the positioning data, heading angle data, and map data; and performing target detection within the candidate region.

[0168] In some embodiments, performing target detection within the candidate region specifically includes: using a deep learning-based target detection model to detect QR code targets within the candidate region; wherein the QR code target detection model is trained based on QR code sample data and is used to identify QR code patterns in the input image.

[0169] In some embodiments, before acquiring the environmental image captured by the wide-angle camera, the method further includes: responding to detecting that the mobile carrier has entered a preset geofence, or receiving a trigger command from a user through a user interaction module.

[0170] In some embodiments, the mobile carrier is a vehicle.

[0171] In some embodiments, after obtaining the identification information of the QR code target from the target image captured by the movable camera, the method further includes: in response to successfully obtaining the identification information of the QR code target from the target image, capturing the user's face image through the in-vehicle camera; performing identity recognition on the user based on the face image; and triggering a payment process in response to successful identity recognition.

[0172] The various technical features in the above embodiments can be combined arbitrarily, as long as there is no conflict or contradiction between the combinations of features. However, due to space limitations, they have not been described one by one. Therefore, the arbitrary combination of various technical features in the above embodiments is also within the scope of this specification.

[0173] Figure 4 This is a flowchart illustrating a QR code-based payment method provided in an embodiment of this specification.

[0174] From a procedural perspective, the entity executing the process can be a program mounted on a terminal device. It can be understood that this method can be executed by any device, equipment, or platform with computing and processing capabilities.

[0175] like Figure 4 As shown, the process may include the following steps: Step 402: Acquire environmental images captured by the wide-angle camera; Step 404: Detect the QR code target from the environmental image; Step 406: Based on the position of the QR code target in the environmental image, send a control signal to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; Step 408: Obtain payment-related information of the QR code target from the target image captured by the movable camera; Step 410: Trigger the payment process based on the payment-related information.

[0176] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps and does not represent the only possible execution order. The order of some steps may be adjusted according to actual needs, or some steps may be omitted. When the claims involve method steps, changes in the order of such steps, or parallel execution between steps, are also within the scope of protection of the claims.

[0177] like Figure 4 As shown, this process is similar to Figure 3 The technical concepts shown are consistent, all based on the linkage architecture of a wide-angle camera and a movable camera to achieve automatic alignment and recognition of QR code targets. Figure 4 The embodiments shown can be understood as Figure 3 The specific improvements to the solution in payment scenarios involve, after obtaining the identification information of the QR code target, further clarifying and extracting payment-related information and triggering the payment process based on that information. In other words, Figure 4 The technical solution shown can be regarded as Figure 3 The extended implementation of the solution in payment applications, its core "wide-angle search - movable alignment - information acquisition" technical concept and Figure 3 While maintaining consistency, it also adds steps for extracting payment-related information and triggering payments to meet the needs of different payment scenarios, thus forming a complete closed loop from target detection to payment completion. Therefore, for... Figure 3 The improved embodiments can also be applied to... Figure 4 The proposed solution will be improved.

[0178] Figure 4 The proposed method first uses a wide-angle camera to capture environmental images and detect QR code targets. Then, based on the QR code target's position in the environmental image, a control signal is sent to a movable camera, driving it to adjust its shooting angle so the QR code target enters its field of view. Next, payment-related information about the QR code target is obtained from the target image captured by the movable camera. Finally, the payment process is triggered based on this payment-related information. By combining the wide-range search of the wide-angle camera with the precise alignment of the movable camera, this solution can automatically lock onto QR code targets and acquire high-definition images over a large area, thereby extracting payment-related information to trigger payment. This forms a complete automated process from target detection to payment execution, enabling the system to adapt to complex scenarios where QR code targets are not fixed in location or are far away. It achieves automatic acquisition of payment information and automatic execution of the payment process without manual user intervention, improving payment convenience and recognition success rate.

[0179] Figure 5 This is a flowchart illustrating a QR code recognition and payment solution for vehicles in a practical application scenario provided in this specification.

[0180] like Figure 5 As shown, this embodiment achieves stable long-distance QR code recognition and payment triggering by constructing a collaborative system of "coarse positioning - active aiming - image quality improvement". The steps include: Step 502: System Trigger.

[0181] The system is activated and begins operation when a vehicle enters the pre-defined geofence area of ​​the parking lot, or when a user manually initiates the payment function through the vehicle interface, voice commands, or physical buttons. The geofence is determined by a ground-sensing coil installed in front of the gate—when a vehicle passes the coil, a trigger signal is generated, which the onboard system receives via dedicated short-range communication or a wireless network, thus activating the identification process.

[0182] Step 504: Coarse localization and region prediction.

[0183] After system activation, the wide-angle camera begins capturing environmental video streams. The processor simultaneously acquires the vehicle's GPS positioning data, IMU heading angle data, and pre-stored parking lot map data, fusing this information to predict the approximate location of the QR code. For example, based on the vehicle's current position and orientation, combined with the map's indication that the gate is located to the right front of the vehicle, the area where the QR code might appear in the wide-angle view (such as the right third of the image) is estimated, and this area is identified as a candidate region for subsequent object detection.

[0184] Step 506: Target detection and pixel coordinate acquisition.

[0185] Within the predicted candidate region, the processor employs a lightweight deep learning-based object detection model to quickly locate suspected QR code patterns. This model is trained using a large amount of QR code sample data, such as the YOLOv5 architecture, enabling real-time detection in an in-vehicle environment. After detecting a QR code, the processor calculates its pixel coordinates and bounding box size in the wide-angle view for use in subsequent steps.

[0186] Step 508: Active aiming and view adjustment.

[0187] The processor sends control signals to the movable camera based on the pixel coordinates of the QR code target in the wide-angle frame to drive it to adjust the shooting angle. In one embodiment, the movable camera is a gimbal camera, supporting direct input of pixel coordinates for driving, with the conversion part completed internally by the gimbal; in another embodiment, the processor can convert the pixel coordinates into the desired attitude angle and then send the angle command. In this way, the movable camera rotates to the target direction, allowing the QR code target to initially enter its field of view.

[0188] Step 510: Dynamic tracking and stable locking.

[0189] During continuous vehicle movement, the processor analyzes the positional changes of the QR code target in multiple consecutive frames of environmental images captured by the wide-angle camera in real time. Based on the positional change information, it generates dynamic compensation commands to drive the movable camera to perform reverse motion compensation, ensuring the QR code target remains stable within the movable camera's field of view. When the positional fluctuation of the QR code target in multiple consecutive frames of target images captured by the movable camera is less than a preset threshold, the system determines that the target has been stably locked. During this process, the system can prompt the driver to reduce the vehicle speed through the user interaction module to assist in target stabilization.

[0190] Step 512: Optical zoom and image enhancement.

[0191] Once the target is stably locked, if the movable camera has optical zoom capabilities, the processor triggers an optical zoom operation to enlarge the QR code target to a sufficiently clear size (e.g., a preset magnification) to improve the resolution of the target image. If the movable camera is a fixed-focal-length telephoto camera, its small field of view itself provides a natural "optical zoom" effect. To further improve image quality, the processor can perform super-resolution reconstruction processing on multiple consecutive frames of target images captured by the movable camera, generating enhanced images through registration and fusion to eliminate image blur caused by slight shaking, changes in lighting, etc.

[0192] Step 514: QR code decoding and recognition.

[0193] The processor calls a decoding library to perform QR code recognition on the enhanced target image, obtaining the recognition information carried within it. The decoding library can use a general QR code decoding algorithm (such as ZXing) to sample and decode the QR code modules in the image, outputting recognition information containing payment links, order numbers, amounts, and other information.

[0194] Step 516: Distance guidance and retry mechanism.

[0195] Optionally, if the pixel size of the QR code in the image is too small or the pixel density of the module is below the recognizable threshold, the processor determines that the target is too far away and the decoding success rate cannot be guaranteed. In this case, distance guidance information is generated and prompted by the user interaction module (such as voice broadcast or display screen) to move the vehicle closer to the QR code. After the vehicle moves, the system automatically re-executes the target detection and alignment process until the recognition conditions are met.

[0196] Step 518: Identity verification and payment triggering.

[0197] After successfully obtaining payment-related information from the QR code, the processor activates the onboard camera to capture the user's facial image, comparing it with a pre-stored facial template or uploading it to the cloud for identity verification. Upon successful identity verification, the processor triggers the payment process based on the payment-related information—either by directly calling the payment interface to complete the deduction, or by outputting a payment confirmation prompt for user confirmation before execution. After payment is completed, the system outputs a payment result notification through the user interaction module. If identity verification fails, the system guides the user to complete the payment using alternative payment methods.

[0198] In addition, the system has corresponding handling mechanisms for various abnormal situations: if no QR code target is detected in the candidate area, the processor can expand the search range to the entire wide-angle image for re-detection; if QR code recognition fails (usually due to excessive distance), the system will prompt the vehicle to approach via voice guidance; if the PTZ camera malfunctions or the network is interrupted, the system will prompt the user to operate manually or use an alternative payment method through the user interaction module.

[0199] Based on the technical solutions provided in the embodiments of this specification, by constructing a dual-camera linkage architecture that coordinates a wide-angle camera and a movable camera, the system can automatically search for QR code targets within a large range. Then, through precise alignment of the movable camera, high-definition images are acquired, forming a complete automated process from target detection to payment triggering. This solution predicts the possible location of the QR code through sensor fusion and real-time image analysis, performing target detection within candidate areas, effectively reducing computational overhead and improving detection efficiency. Through an active visual servo tracking mechanism, relative motion is continuously compensated during vehicle movement, keeping the QR code target stable within the field of view of the movable camera. Through optical zoom and super-resolution reconstruction processing, clear enhanced images are obtained under complex conditions such as long distance, shaking, and low light, providing high-quality image input for subsequent decoding. Based on the synergistic effect of the aforementioned technical features, the system can initiate the recognition process at a distance of, for example, 5 to 15 meters, which is a longer effective recognition distance than solutions that rely solely on a single camera; it can maintain a high recognition success rate even in complex scenarios such as motion and changes in lighting; the system automatically completes all recognition and payment steps as the user approaches, eliminating the need for manual operation and achieving a seamless payment experience; the processing time per vehicle is effectively shortened, which helps alleviate congestion at entrances and exits and improves overall traffic efficiency.

[0200] Based on the same idea, embodiments of this specification also provide apparatus corresponding to the above methods.

[0201] Figure 6 The embodiments provided in this specification correspond to Figure 3 A schematic diagram of the structure of a target recognition device.

[0202] like Figure 6 As shown, the device may include: Image acquisition module 602 is used to acquire environmental images captured by a wide-angle camera; Target detection module 604 is used to detect QR code targets from the environmental image; The signal transmitting module 606 is used to send a control signal to the movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein the field of view of the movable camera is smaller than the field of view of the wide-angle camera. The information recognition module 608 is used to obtain the recognition information of the QR code target from the target image captured by the movable camera, and the recognition information is used to trigger the payment process.

[0203] based on Figure 6 The embodiments of this specification also provide some specific implementation schemes of the method, which are described below.

[0204] In some embodiments, the target recognition method further includes generating the control signal by: obtaining the pixel coordinates of the QR code target in the environmental image; converting the pixel coordinates into a desired attitude angle based on pre-stored camera calibration parameters; generating a control signal containing the desired attitude angle; the control signal being used to control the movable camera to rotate to the desired attitude angle.

[0205] In some embodiments, the target recognition method further includes generating the control signal by: acquiring the pixel coordinates of the QR code target in the environmental image; generating a control signal containing the pixel coordinates; the control signal being used to instruct the movable camera to convert the pixel coordinates into a rotation angle and perform rotation based on the rotation angle.

[0206] In some embodiments, the movable camera has optical zoom capability; before obtaining the identification information of the QR code target from the target image captured by the movable camera, the method further includes: in response to determining that the QR code target has entered the field of view of the movable camera, triggering the movable camera to perform optical zoom operation to improve the resolution of the target image captured by the movable camera.

[0207] In some embodiments, the target recognition method further includes: determining, before triggering the optical zoom operation, that the positional fluctuation of the QR code target in a series of target images captured by the movable camera is less than a preset threshold.

[0208] In some embodiments, obtaining the identification information of the QR code target from the target image captured by the movable camera specifically includes: performing super-resolution reconstruction processing on the target image captured by the movable camera to generate an enhanced image; and parsing the identification information of the QR code target from the enhanced image.

[0209] In some embodiments, the method is applied to a mobile carrier.

[0210] In some embodiments, the target recognition method further includes: during the movement of the mobile carrier, acquiring position change information of the QR code target in multiple consecutive frames of environmental images captured by the wide-angle camera; generating a dynamic compensation instruction based on the position change information; and driving the movable camera to perform reverse motion compensation based on the dynamic compensation instruction, so that the QR code target remains stable within the field of view of the movable camera.

[0211] In some embodiments, the target recognition method further includes: determining whether the QR code target meets preset recognition conditions based on the pixel size or module pixel density of the QR code target in the target image; if the preset recognition conditions are not met, generating distance guidance information to instruct the mobile carrier to move towards the QR code target.

[0212] In some embodiments, the target recognition method further includes: outputting the distance guidance information to the user through a user interaction module.

[0213] In some embodiments, detecting a QR code target in an environmental image captured by the wide-angle camera specifically includes: acquiring the positioning data, heading angle data, and map data of the mobile carrier; predicting a candidate region in the environmental image where the QR code target may appear based on the positioning data, heading angle data, and map data; and performing target detection within the candidate region.

[0214] In some embodiments, performing target detection within the candidate region specifically includes: using a deep learning-based target detection model to detect QR code targets within the candidate region; wherein the QR code target detection model is trained based on QR code sample data and is used to identify QR code patterns in the input image.

[0215] In some embodiments, before acquiring the environmental image captured by the wide-angle camera, the method further includes: responding to detecting that the mobile carrier has entered a preset geofence, or receiving a trigger command from a user through a user interaction module.

[0216] In some embodiments, the mobile carrier is a vehicle.

[0217] In some embodiments, after obtaining the identification information of the QR code target from the target image captured by the movable camera, the method further includes: in response to successfully obtaining the identification information of the QR code target from the target image, capturing the user's face image through the in-vehicle camera; performing identity recognition on the user based on the face image; and triggering a payment process in response to successful identity recognition.

[0218] It is understood that the modules mentioned above refer to computer programs or program segments used to perform one or more specific functions. Furthermore, the distinction between these modules does not imply that the actual program code must also be separate.

[0219] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0220] The above is an illustrative scheme of a target recognition device according to this embodiment. It should be noted that the technical solution of this target recognition device and the technical solution of the target recognition method described above belong to the same concept. For details not described in detail in the technical solution of the target recognition device, please refer to the description of the technical solution of the target recognition method described above.

[0221] Figure 7 The embodiments provided in this specification correspond to Figure 4 A schematic diagram of the structure of a QR code-based payment device.

[0222] like Figure 7 As shown, the device may include: The environmental image acquisition module 702 is used to acquire environmental images captured by the wide-angle camera. QR code detection module 704 is used to detect QR code targets from the environmental image; The control signal sending module 706 is used to send a control signal to the movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein the field of view of the movable camera is smaller than the field of view of the wide-angle camera. The QR code recognition module 708 is used to obtain payment-related information of the QR code target from the target image captured by the movable camera; The payment triggering module 710 is used to trigger the payment process based on the payment-related information.

[0223] It is understood that the modules mentioned above refer to computer programs or program segments used to perform one or more specific functions. Furthermore, the distinction between these modules does not imply that the actual program code must also be separate.

[0224] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0225] The above is an illustrative scheme of a QR code-based payment device according to this embodiment. It should be noted that the technical solution of this QR code-based payment device and the technical solution of the aforementioned QR code-based payment method belong to the same concept. Details not described in detail in the technical solution of the QR code-based payment device can be found in the description of the technical solution of the aforementioned QR code-based payment method.

[0226] Based on the same idea, this specification also provides devices corresponding to the above methods in its embodiments.

[0227] Figure 8 A structural block diagram of a computing device provided according to an embodiment of this specification is shown.

[0228] The computing device 800 includes: Memory 810 and processor 820; The memory 810 is used to store computer programs / instructions, and the processor 820 is used to execute the computer programs / instructions. When the computer programs / instructions are executed by the processor 820, they implement the steps of the target recognition method or the QR code-based payment method.

[0229] Specifically, the components of the computing device 800 include, but are not limited to, a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and the database 850 is used to store data.

[0230] The computing device 800 also includes an access device 840, which enables the computing device 800 to communicate via one or more networks 860. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 840 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0231] In one embodiment of this specification, the above-described components of the computing device 800 and Figure 8 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 8 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0232] The computing device 800 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 800 can also be a mobile or stationary server.

[0233] The processor 820 executes the computer instructions to implement the steps of the target recognition method or the QR code-based payment method.

[0234] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device belongs to the same concept as the target recognition method or the QR code-based payment method. Details not described in detail in the technical solution of the computing device can be found in the descriptions of the target recognition method or the QR code-based payment method.

[0235] An embodiment of this specification also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the target identification method or the QR code-based payment method described above.

[0236] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the target identification method or the QR code-based payment method. Details not described in detail in the technical solution of the storage medium can be found in the descriptions of the technical solutions of the target identification method or the QR code-based payment method.

[0237] An embodiment of this specification also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the target identification method or the QR code-based payment method.

[0238] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product belongs to the same concept as the target recognition method or the QR code-based payment method. Details not described in detail in the technical solution of the computer program product can be found in the descriptions of the target recognition method or the QR code-based payment method.

[0239] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the method, apparatus, and device embodiments, since they are basically similar to the system embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The methods, apparatus, and devices provided in the embodiments of this specification correspond to the methods, therefore, the methods, apparatus, and devices also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding methods, apparatus, and devices will not be repeated here.

[0240] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0241] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0242] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0243] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0244] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0245] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0246] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0247] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0248] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0249] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0250] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0251] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital character versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0252] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0253] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A target recognition system, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; as well as The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger the payment process.

2. The system of claim 1, wherein the processor is configured to generate the control signal in the following manner: Obtain the pixel coordinates of the QR code target in the environmental image; Based on the pre-stored camera calibration parameters, the pixel coordinates are converted into the desired pose angle; Generate a control signal containing the desired attitude angle; The movable camera is configured as follows: In response to the control signal, rotate to the desired attitude angle.

3. The system of claim 1, wherein the processor is configured to generate the control signal in the following manner: Obtain the pixel coordinates of the QR code target in the environmental image; Generate a control signal containing the pixel coordinates; The movable camera is configured as follows: The pixel coordinates are converted into rotation angles, and rotation is performed based on the rotation angles.

4. The system as described in claim 2 or 3, wherein the movable camera is a gimbal camera, configured to rotate in at least two degrees of freedom, pitch and yaw, under the control of the processor.

5. The system as described in claim 1, wherein the movable camera has optical zoom capability; The processor is also configured to: In response to determining that the QR code target has entered the field of view of the movable camera, the movable camera is triggered to perform optical zoom operation to improve the resolution of the target image captured by the movable camera.

6. The system of claim 5, wherein the processor is further configured to: Before triggering the optical zoom operation, it is determined that the position fluctuation of the QR code target in the continuous multi-frame target images captured by the movable camera is less than a preset threshold.

7. The system of claim 1, wherein the processor is configured to acquire the identification information of the QR code target from the target image captured by the movable camera in the following manner: Super-resolution reconstruction processing is performed on the target image captured by the movable camera to generate an enhanced image; The identification information of the QR code target is parsed from the enhanced image.

8. The system as claimed in claim 1, wherein the system is mounted on a mobile carrier.

9. The system of claim 8, wherein the processor is further configured to: During the movement of the mobile carrier, the position change information of the QR code target in the continuous multi-frame environmental images captured by the wide-angle camera is obtained; Generate dynamic compensation instructions based on the position change information; The movable camera is driven to perform reverse motion compensation according to the dynamic compensation command, so that the QR code target remains stable within the field of view of the movable camera.

10. The system of claim 8, wherein the processor is further configured to: Based on the pixel size or module pixel density of the QR code target in the target image, determine whether the QR code target meets the preset recognition conditions; If the preset recognition conditions are not met, distance guidance information is generated to instruct the mobile carrier to move toward the QR code target.

11. The system of claim 10, further comprising a user interaction module communicatively connected to the processor, configured to output the distance guidance information to a user.

12. The system of claim 8, wherein the processor is configured to detect a QR code target from an environmental image captured by the wide-angle camera in the following manner: Acquire the positioning data, heading angle data, and map data of the mobile carrier; Based on the positioning data, heading angle data, and map data, predict the candidate regions in the environmental image where the QR code target will appear; Perform target detection within the candidate region.

13. The system of claim 12, wherein the processor is configured to perform target detection within the candidate region in such a manner as follows: A deep learning-based target detection model is used to detect QR code targets within the candidate region. in, The QR code target detection model is trained based on QR code sample data and is used to identify QR code patterns in input images.

14. The system of claim 8, wherein the processor is further configured to: In response to detecting that the mobile carrier has entered a preset geofence, or receiving a trigger command from the user through the user interaction module, the system is activated to begin executing the step of detecting QR code targets in the environmental images captured by the wide-angle camera.

15. The system as claimed in any one of claims 8 to 14, wherein the mobile carrier is a vehicle.

16. The system of claim 1, wherein the processor is further configured to: In response to successfully obtaining the recognition information of the QR code target from the target image, the user's facial image is captured by the in-carrier camera; The user is identified based on the facial image; Upon successful identity verification, the payment process is triggered.

17. A QR code-based payment system, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; as well as The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; The payment-related information of the QR code target is obtained from the target image captured by the movable camera; The payment process is triggered based on the payment-related information.

18. The system of claim 17, wherein the system is mounted on a mobile carrier, the mobile carrier including a vehicle.

19. A target recognition system, comprising: A wide-angle camera is used to capture environmental images; A movable camera, wherein the field of view of the movable camera is smaller than that of the wide-angle camera, and the movable camera has the ability to adjust the shooting angle; as well as The processor, communicatively connected to the wide-angle camera and the movable camera, is configured to: Detect QR code targets from environmental images captured by the wide-angle camera; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; as well as The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger business processing.

20. A target recognition method, comprising: Acquire environmental images captured by a wide-angle camera; Detect QR code targets from the environmental image; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The identification information of the QR code target is obtained from the target image captured by the movable camera, and the identification information is used to trigger the payment process.

21. A QR code-based payment method, comprising: Acquire environmental images captured by a wide-angle camera; Detect QR code targets from the environmental image; Based on the position of the QR code target in the environmental image, a control signal is sent to the movable camera to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The payment-related information of the QR code target is obtained from the target image captured by the movable camera; The payment process is triggered based on the payment-related information.

22. A target recognition device, comprising: The environmental image acquisition module is used to acquire environmental images captured by the wide-angle camera; A QR code detection module is used to detect QR code targets from the environmental image; A control signal sending module is used to send a control signal to a movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; The QR code recognition module is used to obtain the recognition information of the QR code target from the target image captured by the movable camera, and the recognition information is used to trigger the payment process.

23. A QR code-based payment device, comprising: The environmental image acquisition module is used to acquire environmental images captured by the wide-angle camera; A QR code detection module is used to detect QR code targets from the environmental image; A control signal sending module is used to send a control signal to a movable camera based on the position of the QR code target in the environmental image, so as to drive the movable camera to adjust the shooting angle so that the QR code target enters the field of view of the movable camera; wherein, the field of view of the movable camera is smaller than the field of view of the wide-angle camera; A QR code recognition module is used to obtain payment-related information of the QR code target from the target image captured by the movable camera; The payment triggering module is used to trigger the payment process based on the payment-related information.

24. A computing device, comprising: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 20 and 21.

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