Image acquisition method and system based on drawing board video stream and micro-motion recognition

Abstract

The invention relates to the field of computer vision and image processing, and discloses image acquisition based on drawing board video streaming and micro-motion recognition. The method comprises the following steps: acquiring a video stream of a target object in front of a preset drawing board, and determining a to-be-recognized area corresponding to each to-be-collected part from the video stream; performing micro-action detection on each to-be-identified area, and judging that the real field condition is met under the condition that the detection result of each to-be-identified area meets the preset condition; and determining a target to-be-collected part from all the to-be-collected parts under the condition that the real-to-site condition is met, and intercepting a picture at a position corresponding to the target to-be-collected part from the video stream. According to the method, the extraction precision of the face region is improved, and the recognition accuracy is improved by recognizing fine dynamic changes in combination with a video micro-motion recognition technology; fine dynamic changes can be captured, and the robustness of recognition is improved; and the influence caused by blurred photos or overlarge pixels and the like is solved.

Description

Technical Field

[0001] This application relates to the fields of computer vision and image processing technology, and in particular to an image acquisition method, apparatus, system, storage medium, and computer program product based on drawing board video stream and micro-motion recognition. Background Technology

[0002] The purpose of the background description provided herein is to give an overall background to this application. The statements in this section are merely to provide background information relevant to this application and do not necessarily constitute prior art.

[0003] Under current technology, facial recognition systems typically consist of the following main components:

[0004] Image acquisition device: Generally a camera, used to capture facial images of the target. Cameras can be fixed or mobile to adapt to different application scenarios. Image processing unit: This unit is responsible for receiving the images captured by the camera and performing necessary preprocessing operations, such as grayscale conversion, filtering, and noise reduction, to improve image quality. Facial recognition algorithm: A pre-trained algorithm or model is used to perform facial recognition on the processed images. This typically involves steps such as feature extraction and comparison. Storage and transmission module: Responsible for storing the acquired facial images and recognition results and transmitting them to where they are needed, such as data centers or user terminals.

[0005] The relationships or conditions between the above components are as follows:

[0006] Camera and Image Processing Unit: After capturing an image, the camera transmits the image data to the image processing unit in real time for processing. Image Processing Unit and Face Recognition Algorithm: After preprocessing, the image processing unit transmits the processed image data to the face recognition algorithm for identification. Face Recognition Algorithm and Storage and Transmission Module: The face recognition algorithm transmits the recognition result to the storage and transmission module, which is responsible for storage and transmission. The existing face capture process can be found by referring to [reference needed]. Figure 1 .

[0007] Current facial recognition systems have the following shortcomings to some extent:

[0008] The impact caused by poor facial image quality and low clarity;

[0009] It has certain limitations when handling some complex situations (such as blurry or distorted photos);

[0010] Some photos have excessively large pixel counts (for example, a single photo can be 8MB, 10MB, or even larger), which is not conducive to subsequent compression processing.

[0011] Current facial recognition systems are unable to effectively identify issues such as remote video attendance tracking. Summary of the Invention

[0012] To address the aforementioned problems, this application proposes an image acquisition method, apparatus, storage medium, computer program product, and electronic device based on a whiteboard video stream and micro-motion recognition. The face acquisition method based on whiteboard video stream acquisition and video micro-motion recognition aims to achieve more efficient and accurate face acquisition by combining whiteboard video stream acquisition technology and video micro-motion recognition technology.

[0013] The first aspect of this application provides an image acquisition method based on a whiteboard video stream and micro-motion recognition, applicable to access control systems or visitor systems, including:

[0014] Acquire a video stream of the target object in front of a preset canvas, and determine the regions to be identified corresponding to each part to be captured from the video stream; wherein each part to be captured is a pre-specified part of the target object;

[0015] Micro-motion detection is performed on each region to be identified, and if the detection results of each region to be identified meet the preset conditions, the target object is determined to meet the on-site conditions.

[0016] When the target object meets the on-site conditions, the target part to be acquired is determined from all parts to be acquired, and the image of the corresponding position of the target part to be acquired is captured from the video stream, thereby completing the image acquisition of the target part to be acquired.

[0017] Furthermore, it also includes:

[0018] Based on the image at the location corresponding to the target to be collected, employee information or visit information of the target object is identified from a preset database; wherein, the employee information includes one or more of the following: name, company name, department name, employee ID, and contact number; the visit information includes one or more of the following: visitor name, visitor company name, visitor department name, visitor information, and visitor contact number.

[0019] Furthermore, acquiring the video stream of the target object located in front of the preset canvas includes:

[0020] A video stream of the target object located in front of a preset canvas, captured by a video capture device, is obtained to reduce background noise interference of the target object in the video stream.

[0021] Further, the step of determining the region to be identified includes:

[0022] The region to be identified is determined from the video stream for each part to be captured using a differential algorithm.

[0023] Furthermore, the preset canvas includes a static image background, which is generated according to preset requirements.

[0024] Furthermore, after identifying the employee information or visit information of the target object from the preset database, the method further includes:

[0025] If the employee information of the target object is successfully identified, access control opening information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or an instruction is issued to control the access control system to open the door lock;

[0026] If the visit information of the target object is successfully identified, visit information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or a command is issued to control the access control system to open the door lock.

[0027] A second aspect of this application provides an image acquisition device based on a drawing board video stream and micro-motion recognition, comprising:

[0028] The region to be identified module is used to acquire a video stream of a target object in front of a preset canvas, and to determine the region to be identified corresponding to each part to be captured from the video stream; wherein each part to be captured is a pre-specified part of the target object.

[0029] The determination module is used to perform micro-motion detection on each region to be identified, and determine that the target object meets the on-site conditions if the detection results of each region to be identified meet the preset conditions.

[0030] The target area to be acquired image determination module is used to determine the target area to be acquired from all areas to be acquired when the target object meets the on-site conditions, and to extract the image of the corresponding position of the target area to be acquired from the video stream, thereby completing the image acquisition of the target area to be acquired.

[0031] A third aspect of this application provides an image acquisition system based on a drawing board video stream and micro-motion recognition, comprising:

[0032] A video capture device is used to capture a video stream of a target object positioned in front of a preset canvas.

[0033] The control unit is communicatively connected to the video acquisition device and is used to acquire images of the target part of the target object in the video stream according to the method described above.

[0034] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that can be executed by one or more processors to implement the steps of the method described above.

[0035] A fifth aspect of this application provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described above.

[0036] Compared with the prior art, the advantages or beneficial effects of the technical solution of this application include:

[0037] By employing canvas video stream acquisition technology and using a fixed background as a reference, image processing techniques such as differential algorithms can accurately extract facial regions from videos in real time. This not only reduces background noise interference but also improves the accuracy of facial region extraction. Furthermore, by combining video micro-motion recognition technology, subtle dynamic changes in the facial region are identified, further enhancing the accuracy of facial recognition.

[0038] In complex environments, such as changes in lighting, occlusion, and changes in facial expressions, traditional facial recognition technology often struggles to maintain stable performance. However, this application combines whiteboard video stream acquisition and video micro-motion recognition technology to capture subtle dynamic changes in the face, thereby improving the robustness of facial recognition and enabling accurate facial recognition even in complex environments.

[0039] It greatly enhances the user experience of face capture and avoids the limitations of the user's own mobile operating system and camera. At the same time, it can effectively adjust the captured photos to meet the modeling requirements of downstream access control systems, and solve the problems caused by blurry or excessively large photos. In addition, enterprises do not need to upgrade the hardware of downstream access control equipment and supporting systems, saving enterprise costs and having good universality.

[0040] When applied to access control or visitor systems, it can simultaneously recognize the face and other body parts (such as hands or legs) of relevant personnel, improving the accuracy of determining whether relevant personnel have actually arrived on-site to clock in or visit; moreover, for relevant personnel, there is no need to stop to perform related authentication operations, and the identification or authentication operation can be carried out while the relevant personnel are walking, improving the passage efficiency of relevant personnel. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] It should also be noted that, for ease of description, only the parts relevant to this disclosure are shown in the accompanying drawings. The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions in this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0043] Figure 1 A technical roadmap for a face capture method;

[0044] Figure 2 A flowchart illustrating an image acquisition method based on a canvas video stream and micro-motion recognition, provided in this application embodiment;

[0045] Figure 3 A flowchart of another image acquisition method based on canvas video stream and micro-motion recognition provided in this application embodiment. Detailed Implementation

[0046] The following detailed description of the embodiments of this application, in conjunction with the accompanying drawings, will provide a thorough understanding of how this application uses technical means to solve technical problems and achieve corresponding technical effects, enabling its implementation. The embodiments of this application and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of this application.

[0047] It should be clearly stated that the embodiments described below are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0048] Example 1

[0049] This embodiment provides an image acquisition method based on whiteboard video stream and micro-motion recognition, which can be applied to / used in attendance systems, access control systems or visitor systems.

[0050] In this embodiment, we take company employees or visitors as an example and capture images of their faces.

[0051] Figure 2A flowchart illustrating an image acquisition method based on a canvas video stream and micro-motion recognition, provided for embodiments of this application, is shown below. Figure 2 As shown, the method disclosed in this embodiment includes the following steps:

[0052] Step 210: Obtain the video stream of the target object in front of the preset canvas, and determine the recognition area corresponding to each part to be collected from the video stream; wherein each part to be collected is a pre-specified part of the target object.

[0053] It is understandable that the area to be collected can be one or more pre-specified areas of the target object, and the specific area to be collected can be set according to actual needs.

[0054] Specifically, when the target object is a company employee / visitor, the area to be collected can be specified as one or more of the company employee / visitor's face, hands, and legs.

[0055] In some possible cases, when the target object is another animal, the part to be collected can be specified as a specific part of that animal.

[0056] The following example uses the recognition of human face and hands, with the face being the target area to be collected.

[0057] In some embodiments, prior to acquiring the video stream of the target object located in front of a preset canvas, the method further includes:

[0058] A static image background is generated according to preset requirements, and the preset canvas is generated based on the static image background.

[0059] Optionally, preset requirements can be set according to actual conditions. For example, the preset requirement can be set to include the company logo and the current time; that is, the static image background generated according to this preset requirement will include the company logo and the current time.

[0060] Optionally, the static image background can contain two parts of information: one part can be fixed image information, such as a company logo; the other part can be a time display area.

[0061] Based on this, the attendance, visit, or access control information of relevant personnel can be traced back to a certain extent by using the static image background in the video stream.

[0062] It should be noted that the preset canvas can be displayed on an electronic display screen or on a mobile terminal used for video shooting. At the display level, in the preset canvas, the video stream of the target object is located on top of the static image background.

[0063] In some embodiments, acquiring the video stream of the target object located in front of a preset canvas includes:

[0064] A video stream of the target object located in front of a preset canvas, captured by a video capture device, is obtained to reduce background noise interference of the target object in the video stream.

[0065] Optionally, the video stream of the target object captured by the video capture device can be mapped onto a preset canvas to obtain the video stream of the target object in front of the preset canvas.

[0066] In some possible cases, the preset canvas can also be a paper canvas with a static image background. The video stream of the target object in front of the preset canvas is captured by a video capture device, thereby obtaining the video stream of the target object in front of the preset canvas.

[0067] In some embodiments, the step of determining the region to be identified includes:

[0068] The region to be identified is determined from the video stream for each part to be captured using a differential algorithm.

[0069] Among them, the corresponding recognition area for each part to be collected can be determined by some preset image processing methods; the preset image processing methods may include differential algorithms or other image processing methods, which can be selected according to actual needs.

[0070] This embodiment employs a canvas video stream acquisition technique, which uses a high-definition camera to capture the video stream of the user in front of a canvas in real time. Unlike traditional video stream acquisition methods, this technique utilizes the fixed background of the canvas as a reference and extracts the user's face region in real time using differential algorithms or other image processing techniques. This method significantly reduces background noise interference and improves the accuracy of face region extraction.

[0071] Canvas video stream acquisition can reduce background noise interference and improve the accuracy of face region extraction by using a canvas with a fixed background.

[0072] Step 220: Perform micro-motion detection on each region to be identified, and if the detection results of each region to be identified meet the preset conditions, determine that the target object meets the on-site conditions.

[0073] After extracting the facial region, video micro-motion recognition technology is further used to identify dynamic changes in the user's face. This technology analyzes pixel-level changes in the facial region, such as blinking and lip movements, to identify the user's identity or state. This method can capture subtle dynamic changes in the face, improving the accuracy and robustness of facial recognition.

[0074] Furthermore, after extracting the hand area, video micro-motion recognition technology is used to identify the dynamic changes in the user's hands. Then, based on the dynamic changes in the face and hands, it is determined whether the current user meets the conditions for being present at the scene.

[0075] Video micro-motion recognition can capture subtle dynamic changes in the area to be collected, improving the accuracy and robustness of the recognition of the area to be collected.

[0076] Optionally, the preset conditions can be set to: each area to be identified has undergone a certain degree of movement. For example, for the face: the eyes blinked, or the lips closed or opened; for the hands: the fingers wobbled slightly. The specific settings can be adjusted according to the actual situation or the specific part of the area to be identified.

[0077] Finally, the whiteboard video stream acquisition technology and video micro-motion recognition technology are combined to achieve more efficient and accurate face capture. Specifically, the user's face region is first extracted using whiteboard video stream acquisition technology, and then video micro-motion recognition technology is used to dynamically analyze the face region to identify the user's identity or status. This processing solution can achieve real-time and accurate face capture and recognition in complex environments.

[0078] Combining whiteboard video stream acquisition with video micro-motion recognition can achieve more efficient and accurate face acquisition, improving the overall performance of the face recognition system.

[0079] Step 230: If the target object meets the on-site conditions, determine the target part to be acquired from all parts to be acquired, and capture the image of the corresponding position of the target part to be acquired from the video stream, thereby completing the image acquisition of the target part to be acquired.

[0080] Specifically, if it is determined that the user is actually present at the scene, the target area to be collected is determined from the user's face and hands (in this embodiment, the target area to be collected is the face, and the target area to be collected can be set according to actual needs), and then the image of the corresponding position of the face is captured from the video stream.

[0081] In some embodiments, it also includes:

[0082] Based on the image at the location corresponding to the target to be collected, employee information or visit information of the target object is identified from a preset database; wherein, the employee information includes one or more of the following: name, company name, department name, employee ID, and contact number; the visit information includes one or more of the following: visitor name, visitor company name, visitor department name, visitor information, and visitor contact number.

[0083] When applied to a community access control system, based on the image at the location corresponding to the target to be collected, the resident information of the target object is identified from a preset database. The resident information includes one or more of the following: name, building number, unit number, door number, and telephone number.

[0084] It should be noted that a preset database stores detailed information about the target objects corresponding to the target location. Based on the determined target location, the detailed information of the corresponding target object can be identified or retrieved from this preset database.

[0085] In some embodiments, after identifying the employee information or visit information of the target object from a preset database, the method further includes:

[0086] If the employee information of the target object is successfully identified, access control opening information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or an instruction is issued to control the access control system to open the door lock;

[0087] If the visit information of the target object is successfully identified, visit information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or a command is issued to control the access control system to open the door lock.

[0088] Furthermore, based on the detailed information of the target object obtained from the target area to be collected (employee information, owner information, or visit information, or the detailed information of the target object may not be obtained), the target object is further judged, and the corresponding operation is performed based on the judgment result.

[0089] For example, if the determination result is: the employee clocked in on-site, a successful clock-in message will be displayed, clock-in information will be generated based on the current time and the corresponding location of the target data collection area, and this information will be saved in the clock-in system. If the determination result is: the person is a resident of this community, the access control information will be saved, and the access control system will be controlled to perform the unlocking operation. If the determination result is: the visitor is a registered visitor, a visitor matching success message will be displayed, and the visitor information will be saved in the visitor system. The access control system can also be controlled to perform the unlocking operation. If the determination result is abnormal (e.g., detailed information about the target object cannot be obtained), the corresponding error message will be displayed, and the current operation process will end.

[0090] When applied to a check-in system, a high-definition camera can capture real-time video of people passing in front of a preset canvas. Based on the captured video stream, the system can perform corresponding operations such as employee attendance confirmation, which can efficiently check in and improve traffic flow.

[0091] Furthermore, for a better understanding of the technical solution of this application, please refer to... Figure 3, Figure 3 A flowchart of another image acquisition method based on canvas video stream and micro-motion recognition provided in this application embodiment.

[0092] The method disclosed in this application combines canvas video stream processing with micro-motion recognition analysis to ensure high-quality captured facial images and provide a good user experience. First, a "canvas" or video display area is defined. Then, a high-definition camera captures the video stream of the user in front of the canvas in real time, and micro-motion recognition is used to implement liveness detection technology to verify the authenticity of the face. This avoids the limitations of various mobile operating systems (Android) and cameras, allowing for flexible acquisition of facial photos and adjustment of clarity. The use of HTML5 Canvas to capture video streams improves processing speed. It also works stably even under interference from changing lighting conditions and facial expressions. This image acquisition method not only improves the accuracy and security of facial recognition but also optimizes resource usage, enhancing the stability and practicality of the application.

[0093] Example 2

[0094] Based on the foregoing embodiments, the image acquisition method based on canvas video stream and micro-motion recognition disclosed in Embodiment 1 will be further explained and illustrated in a specific application manner.

[0095] Under current technology, temporary visitor services generally require visitors to register their faces on their mobile phones to form a standardized facial model before it is sent to the downstream access control system so that the downstream access control system can provide the corresponding personnel permissions.

[0096] Under current technology, the system directly uses the phone's camera to take facial photos, processes the photos simply, and then provides them to the downstream access control system.

[0097] This method requires visitors to adjust their phone position to be as level as possible with their eyes when taking the photo, and the background color must be a single, solid color, unaffected by strong light. For a large number of temporary visitors, this traditional facial photo collection method results in a success rate of only 50% for the first photo taken, leading to low photo quality. Visitors need to repeatedly take photos to meet the facial modeling requirements of the downstream access control system, creating an unfriendly experience for visitors.

[0098] After improving the face capture method based on canvas video stream acquisition and video micro-motion recognition, the success rate of visitor first-time photo modeling has increased to over 90%, and the quality of the photos has been effectively improved. Under normal circumstances, visitors only need to take one photo to meet the modeling requirements of the downstream access control system, and the user experience has been greatly improved.

[0099] Example 3

[0100] Based on the foregoing embodiments, this embodiment provides an image acquisition device based on drawing board video stream and micro-motion recognition.

[0101] This device embodiment can be used to execute the method embodiment of this application. For details not disclosed in this device embodiment, please refer to the method embodiment of this application. The device disclosed in this embodiment includes:

[0102] The region to be identified module is used to acquire a video stream of a target object in front of a preset canvas, and to determine the region to be identified corresponding to each part to be captured from the video stream; wherein each part to be captured is a pre-specified part of the target object.

[0103] The determination module is used to perform micro-motion detection on each region to be identified, and determine that the target object meets the on-site conditions if the detection results of each region to be identified meet the preset conditions.

[0104] The target area to be acquired image determination module is used to determine the target area to be acquired from all areas to be acquired when the target object meets the on-site conditions, and to extract the image of the corresponding position of the target area to be acquired from the video stream, thereby completing the image acquisition of the target area to be acquired.

[0105] In some embodiments, it also includes:

[0106] The identification module is used to identify detailed information about the target object from a preset database based on the target part to be collected.

[0107] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computing devices, either centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. Furthermore, in some cases, the steps shown or described can be performed in a different order than presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of each module in the image acquisition device can be referred to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0109] Example 4

[0110] Based on the foregoing embodiments, this embodiment provides an image acquisition system based on canvas video stream and micro-motion recognition.

[0111] The system disclosed in this embodiment includes:

[0112] A video capture device is used to capture a video stream of a target object positioned in front of a preset canvas.

[0113] The control unit is communicatively connected to the video acquisition device and is used to acquire images of target parts of the target object in the video stream according to the method described in the foregoing embodiments.

[0114] In some embodiments, it also includes:

[0115] The server has a preset database and is used to identify and return detailed information of the target part in response to a detailed information identification command.

[0116] Example 5

[0117] This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement the method steps as described in the foregoing method embodiments; these steps will not be repeated here.

[0118] Computer-readable storage media may individually include computer programs, data files, data structures, etc., or combinations thereof. The computer-readable storage media or computer program may be specifically designed and understood by those skilled in the art of computer software, or the computer-readable storage media may be known and available to those skilled in the art of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. Furthermore, computer-readable storage media may be distributed across networked computer systems, allowing for the decentralized storage and execution of program code or computer programs.

[0119] Example 6

[0120] This embodiment provides a computer program product. The computer program product includes a computer program or instructions, which, when executed by a processor, implement all or part of the steps of the method as described in the foregoing method embodiments; these will not be repeated here.

[0121] Furthermore, the computer program product may include one or more computer-executable components configured to perform the embodiments when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium thereof, the computer program containing program code for performing any of the methods in the embodiments of this disclosure. In such embodiments, the computer program may be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0122] Example 7

[0123] This embodiment provides an electronic device that may include one or more processors, a memory, a multimedia component, an input / output (I / O) interface, and a communication component.

[0124] One or more processors are used to execute all or part of the steps as described in the foregoing method embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0125] One or more processors may be implemented as Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and are used to perform the methods as described in the foregoing method embodiments.

[0126] Memory can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0127] The multimedia component may include a screen, which may be a touchscreen, and an audio component for outputting and / or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0128] An I / O interface provides an interface between one or more processors and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual or physical buttons.

[0129] Communication components are used for wired or wireless communication between the electronic device and other devices. Wired communication includes communication via network ports, serial ports, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or more combinations thereof.

[0130] It should also be understood that the methods or systems disclosed in the embodiments provided in this application can also be implemented in other ways. The method or system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions, and operations of possible implementations of methods and systems according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, computer program segment, or part of a computer program, which includes one or more computer programs for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings, and may actually be executed substantially in parallel. They may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer programs.

[0131] In this application, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, 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 process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "including one..." does not exclude the presence of other identical elements in the process, method, apparatus, or device that includes the element; the use of terms such as "first" and "second" is for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the number or sequence of the indicated technical features; in the description of this application, unless otherwise stated, the terms "multiple" or "many" mean at least two; if a server is described, it should be noted that a server can be an independent physical server or terminal, or a server cluster consisting of multiple physical servers, or a cloud server capable of providing basic cloud computing services such as cloud servers, cloud databases, cloud storage, and CDN; if a smart terminal or mobile device is described in this application, it should be noted that a smart terminal or mobile device can be a mobile phone, tablet computer, smartwatch, netbook, wearable electronic device, personal digital assistant (PDA), augmented reality (AR) device, virtual reality (VR) device, smart TV, smart speaker, personal computer (PC). Computer (PC) etc., but not limited to these, this application does not make any special restrictions on the specific form of smart terminals or mobile devices.

[0132] Finally, it should be noted that in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "a single example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0133] Although embodiments of this application have been shown and described above, it is to be understood that the above embodiments are exemplary and the content is only for the purpose of facilitating understanding of this application, and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this application, but the scope of protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. An image acquisition method based on canvas video stream and micro-motion recognition, characterized in that, Applied to access control systems or visitor systems, the method includes: Acquire a video stream of the target object in front of a preset canvas, and determine the regions to be identified corresponding to each part to be captured from the video stream; wherein each part to be captured is a pre-specified part of the target object; Micro-motion detection is performed on each region to be identified, and if the detection results of each region to be identified meet the preset conditions, the target object is determined to meet the on-site conditions. When the target object meets the on-site conditions, the target part to be acquired is determined from all parts to be acquired, and the image of the corresponding position of the target part to be acquired is captured from the video stream, thereby completing the image acquisition of the target part to be acquired.

2. The image acquisition method based on canvas video stream and micro-motion recognition according to claim 1, characterized in that, Also includes: Based on the image at the location corresponding to the target area to be collected, employee information or visit information of the target object is identified from a preset database; wherein... The employee information includes one or more of the following: name, company name, department name, employee ID, and contact number; the visit information includes one or more of the following: visitor's name, visitor's company name, visitor's department name, visitor information, and visitor's contact number.

3. The image acquisition method based on canvas video stream and micro-motion recognition according to claim 1, characterized in that, The acquisition of the video stream in front of the preset canvas where the target object is located includes: A video stream of the target object located in front of a preset canvas, captured by a video capture device, is obtained to reduce background noise interference of the target object in the video stream.

4. The image acquisition method based on canvas video stream and micro-motion recognition according to claim 1, characterized in that, The step of determining the region to be identified includes: The region to be identified is determined from the video stream for each part to be captured using a differential algorithm.

5. The image acquisition method based on canvas video stream and micro-motion recognition according to any one of claims 1 to 4, characterized in that, Before acquiring the video stream of the target object in front of the preset canvas, the method further includes: A static image background is generated according to preset requirements, and the preset canvas is generated based on the static image background.

6. The image acquisition method based on canvas video stream and micro-motion recognition according to claim 2, characterized in that, After identifying the employee information or visit information of the target object from the preset database, the method further includes: If the employee information of the target object is successfully identified, access control opening information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or an instruction is issued to control the access control system to open the door lock; If the visit information of the target object is successfully identified, visit information is generated based on the current time and the image at the corresponding location of the target to be collected, and / or a command is issued to control the access control system to open the door lock.

7. An image acquisition device based on a whiteboard video stream and micro-motion recognition, characterized in that, include: The region to be identified module is used to acquire a video stream of a target object in front of a preset canvas, and to determine the region to be identified corresponding to each part to be captured from the video stream; wherein each part to be captured is a pre-specified part of the target object. The determination module is used to perform micro-motion detection on each region to be identified, and determine that the target object meets the on-site conditions if the detection results of each region to be identified meet the preset conditions. The target area to be acquired image determination module is used to determine the target area to be acquired from all areas to be acquired when the target object meets the on-site conditions, and to extract the image of the corresponding position of the target area to be acquired from the video stream, thereby completing the image acquisition of the target area to be acquired.

8. An image acquisition system based on canvas video stream and micro-motion recognition, characterized in that, include: A video capture device is used to capture a video stream of a target object positioned in front of a preset canvas. The control unit is communicatively connected to the video acquisition device and is used to acquire images of the target part of the target object in the video stream using the method according to any one of claims 1 to 6.

9. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the method as described in any one of claims 1 to 6.

Images