Method and system for determining exposure parameter of imaging device

Abstract

Provided in the embodiments of the present description are a system and method for determining an exposure parameter of an imaging device. The method may comprise acquiring an optical flow velocity of each of a plurality of pixels of a current image frame, wherein the current image frame may be collected by an imaging device. The method may comprise determining a current brightness and a current shutter upper limit of the imaging device on the basis of the optical flow velocity of each pixel. The method may comprise determining a target exposure parameter of the imaging device on the basis of the current brightness and the current shutter upper limit.

Description

A method and system for determining exposure parameters of an imaging device Cross-references

[0001] This application claims priority to Chinese Patent Application No. 202510204567.9, filed on February 24, 2025, entitled “Exposure Adjustment Method, Apparatus and Storage Medium Based on Motion Judgment”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This specification relates to the field of imaging, and more particularly to a method and system for determining exposure parameters of an imaging device. Background Technology

[0003] During image acquisition using imaging equipment, existing automatic exposure (AE) technology adjusts the equipment's exposure parameters whenever a bright or dark target appears in the imaging area or on the display screen. Frequent adjustments to exposure parameters can cause screen flickering and result in poor image quality. Furthermore, the image acquisition process may involve different shooting scenarios, increasing the complexity of adjusting exposure parameters and further reducing image quality. To solve these problems, technicians typically need to manually adjust the shutter speed limit of the imaging equipment after installation, which is very time-consuming and labor-intensive. Summary of the Invention

[0004] One embodiment of this specification provides a method for determining exposure parameters of an imaging device. The method may include acquiring the optical flow velocity of each pixel in a plurality of pixels of a current frame image. The current frame image may be acquired by the imaging device. The method may include determining the current brightness and current shutter speed limit of the imaging device based on the optical flow velocity of each pixel. The method may further include determining target exposure parameters of the imaging device based on the current brightness and current shutter speed limit.

[0005] One embodiment of this specification provides a system for determining exposure parameters of an imaging device. The system may include a storage device storing computer instructions; and a processor connected to the storage device. When the computer instructions are executed, the processor causes the system to perform the following operations: acquiring the optical flow velocity of each pixel in a plurality of pixels of a current frame image, the current frame image being acquired by the imaging device; determining the current brightness and current shutter speed limit of the imaging device based on the optical flow velocity of each pixel; and determining the target exposure parameters of the imaging device based on the current brightness and current shutter speed limit.

[0006] One embodiment of this specification provides a non-transitory computer-readable medium. The non-transitory computer-readable medium may include executable instructions. When the executable instructions are executed by at least one processor, the at least one processor performs a method for determining exposure parameters of an imaging device. The method may include acquiring the optical flow velocity of each pixel among a plurality of pixels in a current frame image. The current frame image may be acquired by an imaging device. The method may include determining a current brightness and a current shutter speed limit of the imaging device based on the optical flow velocity of each pixel. The method may further include determining target exposure parameters of the imaging device based on the current brightness and the current shutter speed limit. Attached Figure Description

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0008] Figure 1 is a schematic diagram of an application scenario of an exemplary image acquisition system according to some embodiments of this specification;

[0009] Figure 2 is a block diagram of an exemplary processing device according to some embodiments of this specification;

[0010] Figure 3 is a flowchart illustrating an exemplary process for determining target exposure parameters of an imaging device according to some embodiments of this specification;

[0011] Figure 4 is a flowchart illustrating an exemplary process for obtaining the optical flow velocity of each pixel in a plurality of pixels of a current frame image, according to some embodiments of this specification.

[0012] Figure 5 is an exemplary data conversion diagram illustrating some embodiments according to this specification;

[0013] Figure 6 is a flowchart illustrating an exemplary process for switching the operating mode of an imaging device according to some embodiments of this specification;

[0014] Figure 7 is a schematic diagram of an exemplary process for switching the operating mode of an imaging device according to some embodiments of this specification;

[0015] Figure 8 is a schematic diagram of exemplary mode switching thresholds according to some embodiments of this specification;

[0016] Figure 9 is a schematic diagram of the structure of an exemplary electronic device according to some embodiments of this specification;

[0017] Figure 10 is a schematic diagram of the structure of an exemplary computer-readable storage medium according to some embodiments of this specification. Detailed Implementation

[0018] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0019] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0020] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0021] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0022] The principle behind existing after-effect (AE) technology is primarily to maintain the brightness of the original image data close to the target brightness by controlling shooting parameters such as exposure gain, exposure time, and aperture when the ambient light brightness changes, thus avoiding overexposure or underexposure. However, in some application scenarios, such as image acquisition and detection of traffic roads, the focus is often on small individual targets such as license plates or objects inside vehicles. Once a large object, such as a white or dark-colored vehicle, appears in the imaging device's shooting area or on the display screen, occupying a large area of ​​the display screen (affecting image brightness), existing AE technology will frequently adjust the exposure parameters of the imaging device, leading to problems such as screen flickering and poor image quality. Furthermore, the image acquisition process may involve different shooting scenarios, increasing the complexity of adjusting exposure parameters and further reducing image quality. For example, in nighttime scenes, if the same exposure gain limit is used as in daytime scenes, high-speed vehicles in the image will exhibit severe motion blur, and the overall image brightness may be insufficient. In this case, after installing the imaging device, the shutter speed limit can only be adjusted manually, which is not only time-consuming and laborious but also inefficient.

[0023] This specification provides a method and system for determining exposure parameters of an imaging device through several embodiments. The method acquires the optical flow velocity of each pixel in a current frame image. The current frame image can be acquired by the imaging device. The method determines the current brightness and current shutter speed limit of the imaging device based on the optical flow velocity of each pixel. The method can also determine the target exposure parameters of the imaging device based on the target brightness and the current shutter speed limit. This enables the imaging device to maintain image sharpness while meeting the target brightness. Furthermore, since the current brightness and current shutter speed limit of the imaging device are determined based on the optical flow velocity of the pixels, motion-based exposure adjustment is achieved, avoiding interference from high-speed moving objects in the exposure adjustment process and optimizing the effect of automatic exposure adjustment.

[0024] Figure 1 is a schematic diagram of an application scenario of an exemplary image acquisition system 100 according to some embodiments of this specification.

[0025] As shown in Figure 1, in some embodiments, the image acquisition system 100 may include an imaging device 110, a processing device 120, and a storage device 130. Multiple components in the image acquisition system 100 can be interconnected via a network (not shown). For example, the imaging device 110 and the processing device 120 can be connected or communicate via a network. Similarly, the processing device 120 and the storage device 130 can be connected or communicate via a network. In some embodiments, the connections between the components in the image acquisition system 100 are variable. For example, the imaging device 110 may be directly connected to the storage device 130.

[0026] Imaging device 110 can be used to acquire image data (e.g., images, videos, etc.) of a target scene. Exemplary imaging devices may include cameras, optical sensors, radar sensors, structured light scanners, etc., or any combination thereof. For example, imaging device 110 may include devices capable of capturing optical data, such as cameras (e.g., depth cameras, stereo triangulation cameras, etc.) and optical sensors (e.g., red-green-blue-depth (RGB-D) sensors, etc.). As another example, imaging device 110 may include devices capable of acquiring point cloud data, such as laser imaging devices (e.g., phase laser acquisition devices, point laser acquisition devices, etc.). Point cloud data may include multiple data points, each data point representing a physical point on the surface of an object in the target scene, and one or more feature values ​​of the physical point (e.g., feature values ​​related to the position and / or composition of the physical point) may be used to describe the object in the target scene. Point cloud data can be used to reconstruct an image of the target scene. As yet another example, imaging device 110 may include devices capable of acquiring position data and / or depth data of objects in the target scene, such as structured light scanners, time-of-flight (TOF) devices, optical triangulation devices, stereo matching devices, etc., or any combination thereof. The acquired location and / or depth data can be used to reconstruct images of the target scene.

[0027] The target scene refers to the usage scenario in which the imaging device 110 is located. In some embodiments, the imaging device 110 can be installed in the target scene in a detachable or non-detachable manner. For example, the imaging device 110 can be detachably mounted on a guardrail or street lamp along a highway.

[0028] Processing device 120 can process data and / or information obtained from imaging device 110, storage device 130, or other components of image acquisition system 100. For example, processing device 120 can acquire the current frame image and / or the previous frame image from imaging device 110. As another example, processing device 120 can acquire the optical flow velocity of each pixel in a plurality of pixels of the current frame image. As yet another example, processing device 120 can determine the target brightness and current shutter speed limit of imaging device 110 based on the optical flow velocity of each pixel. As yet another example, processing device 120 can determine the target exposure parameters of imaging device 110 based on the target brightness and the current shutter speed limit. In some embodiments, processing device 120 can be a single server or a group of servers. The server group can be centralized or distributed. In some embodiments, processing device 120 can be local or remote. For example, processing device 120 can access information and / or data from imaging device 110 and / or storage device 130 via network 120. For example, processing device 120 can be directly connected to imaging device 110 and / or storage device 130 to access information and / or data. In some embodiments, processing device 120 can be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc., or any combination thereof.

[0029] In some embodiments, the processing device 120 may include one or more processors (e.g., a single-chip processor or a multi-chip processor). By way of example only, the processing device 120 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), an image processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or any combination thereof. In some embodiments, the processing device 120 may be part of the imaging device 110. For example, the processing device 120 may be integrated within the imaging device 110 for determining target exposure parameters of the imaging device 110.

[0030] Storage device 130 may store data, instructions, and / or any other information. For example, storage device 130 may store image data acquired by imaging device 110 (e.g., current frame image, previous frame image) and related information. In some embodiments, storage device 130 may store data obtained from imaging device 110 and / or processing device 120. In some embodiments, storage device 130 may store data and / or instructions used by processing device 120 to perform or use in order to accomplish the exemplary methods described herein. In some embodiments, storage device 130 may include mass storage, removable storage, volatile read-write storage, read-only storage (ROM), or any combination thereof. In some embodiments, storage device 130 may be implemented on a cloud platform. In some embodiments, storage device 130 may be part of processing device 120.

[0031] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art based on the content of this specification. Features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain other and / or alternative exemplary embodiments. However, these changes and modifications will not depart from the scope of this specification. For example, storage device 130 may be a data storage device including cloud computing platforms (e.g., public cloud, private cloud, community cloud, and hybrid cloud). As another example, image acquisition system 100 may also include any suitable network capable of facilitating information and / or data exchange within image acquisition system 100. In some embodiments, at least one component of image acquisition system 100 (e.g., imaging device 110, processing device 120, storage device 130) can exchange information and / or data with at least one other component in image acquisition system 100 via a network. In some embodiments, network 120 may include at least one network access point.

[0032] Figure 2 is a block diagram of an exemplary processing device 120 according to some embodiments of this specification. The processing device 120 may include an acquisition module 210 and a determination module 220.

[0033] The acquisition module 210 can be used to acquire the optical flow velocity of each pixel among multiple pixels in the current frame image. The current frame image can be acquired by an imaging device (e.g., imaging device 110). For more information on acquiring the optical flow velocity of each pixel, please refer to step 302 of Figure 3 and its related description.

[0034] The determination module 220 can be used to determine the current brightness and current shutter speed limit of the imaging device based on the optical flow velocity of each pixel. The current brightness refers to the brightness of the current frame image. The current shutter speed limit refers to the fastest shutter speed (i.e., the shortest exposure time) allowed by the imaging device, determined based on the optical flow velocity of the current frame image. More information on determining the target brightness and current shutter speed limit can be found in step 304 of Figure 3 and its related description.

[0035] The determining module 220 can also be used to determine the target exposure parameters of the imaging device based on the current brightness and the current shutter speed limit. Target exposure parameters refer to the parameter values ​​that the imaging device (e.g., the image sensor of the imaging device) needs to set or adjust in order to achieve the target brightness. Target exposure parameters may include target shutter speed, target exposure gain, etc., or any combination thereof. Target brightness refers to the image brightness that the imaging device (e.g., the automatic exposure system of the imaging device) expects to achieve (i.e., the image brightness that needs to be adjusted). For more information on determining the target exposure parameters of the imaging device, please refer to step 306 in Figure 3 and its related description.

[0036] Each module in the aforementioned processing device 120 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device, or stored in the storage device 130 of the computer device as software, so that the processing device 120 can invoke and execute the operations corresponding to each module.

[0037] It should be noted that the above description of the image acquisition system 100 and its modules is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from this principle. For example, the acquisition module 210 and the determination module 220 disclosed in Figure 2 may be different modules within the same system, or a single module may implement the functions of the two modules described above. Furthermore, the modules in the image acquisition system 100 may share a single storage module, or each module may have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0038] Figure 3 is a flowchart of an exemplary process 300 for determining target exposure parameters of an imaging device according to some embodiments of this specification. In some embodiments, process 300 may be executed by image acquisition system 100. For example, process 300 may be stored in a storage device (e.g., storage device 130) in the form of an instruction set (e.g., an application program). In some embodiments, processing device 120 (e.g., one or more modules shown in Figure 2) may execute the instruction set and accordingly instruct one or more components of image acquisition system 100 to execute process 300.

[0039] In step 302, processing device 120 (e.g., acquisition module 210) can acquire the optical flow velocity of each pixel among multiple pixels of the current frame image. The current frame image can be acquired by an imaging device (e.g., imaging device 110).

[0040] The current frame image refers to the image that needs to be analyzed, acquired by the imaging device in the current scene (also known as the target scene). For example, the imaging device can acquire consecutive frame images (or video data), and the processing device 120 can determine the current frame image from the consecutive frame images (or video data). As an example only, the processing device 120 can use the last frame image in a consecutive frame image as the current frame image. Another example is that the processing device 120 can use a frame image in a consecutive frame image that meets preset conditions as the current frame image. Preset conditions may include, for example, the image contains a target object (e.g., a vehicle), the image is not the first frame in a consecutive frame image, and the difference between the image and adjacent frame images (e.g., brightness difference, position difference) is less than a difference threshold. Preset conditions can be manually set or automatically determined by the system.

[0041] The current scene refers to the usage environment of the imaging device. For example, the current scene can include highway scenes, urban road scenes, rural road scenes, bridge scenes, etc. It can also include open-air road scenes, tunnel scenes, etc. Furthermore, the current scene can include daytime scenes, nighttime scenes, etc.

[0042] In some embodiments, the current frame image may be a two-dimensional (2D) image or a three-dimensional (3D) image.

[0043] In some embodiments, the processing device 120 can acquire image data of the current scene from the imaging device and generate a current frame image based on the image data. For example, when the imaging device is a camera, the processing device 120 can acquire optical data from the camera and generate a current frame image based on the optical data. As another example, when the imaging device is a laser imaging device, the processing device 120 can acquire point cloud data from the laser imaging device and generate a current frame image based on the point cloud data. Yet another example, when the imaging device is a depth camera, the processing device 120 can acquire depth data from the depth camera and generate a depth image as the current frame image based on the depth data.

[0044] In some embodiments, the processing device 120 may directly obtain the current frame image from the imaging device or the storage device (e.g., storage device 130).

[0045] In some embodiments, the processing device 120 may process the original image data to obtain the current frame image. For example, the processing device 120 may perform preprocessing operations (e.g., image segmentation, resizing, image normalization, etc.) on the original image determined based on the original image data to determine the current frame image. The processing device 120 may further perform other steps in process 300 on the preprocessed current frame image. For illustrative purposes, the execution process of process 300 is described below using the original current frame image as an example.

[0046] Optical flow is the visual motion pattern caused by the movement of objects in an image. It can be used to describe the apparent motion of pixels across consecutive frames, resulting from the movement of the subject and / or the relative motion between the imaging device and the scene. For example, optical flow can be represented as a motion vector field of pixels in an image.

[0047] Optical flow velocity characterizes the displacement (e.g., distance and direction) of each pixel (or image patch) from the previous frame to the current frame in a given frame. Accordingly, the unit of optical flow velocity can be pixels per frame. That is, the optical flow velocity of each pixel represents the distance (in pixels) that the pixel has moved in the image plane in which direction within the time interval between two adjacent frames.

[0048] In some embodiments, the optical flow velocity of each pixel in a plurality of pixels of the current frame image can be represented individually. Alternatively, the optical flow velocities of at least a subset of pixels in a plurality of pixels of the current frame image can be represented in combination. For example, the optical flow velocities of at least a subset of pixels in a plurality of pixels of the current frame image can be represented in the form of a set, a matrix, or the like.

[0049] In some embodiments, the processing device 120 can acquire the optical flow velocity of each pixel among multiple pixels in the current frame image. For example, the processing device 120 can use an optical flow method to acquire the optical flow velocity of each pixel among multiple pixels in the current frame image. An optical flow method is a method that utilizes the temporal changes of pixels in an image sequence and the correlation between adjacent frame images to find the correspondence between the previous frame image and the current frame image, thereby calculating the motion information of objects between adjacent frame images. Exemplary optical flow methods may include sparse optical flow methods (e.g., the Lucas-Kanade algorithm, pyramid optical flow method), dense optical flow methods (e.g., the Horn-Schunck algorithm), deep learning-based optical flow methods, and any combination thereof.

[0050] For example, processing device 120 can acquire statistical information generated by the image signal processor of the imaging device, and based on the statistical information, acquire the optical flow velocity of each pixel among multiple pixels in the current frame image. An image signal processor (ISP) is a dedicated chip or circuit module specifically designed to process the raw signal output by an image sensor and convert it into a color image. ISPs can be used for various processing tasks such as exposure, white balance, and noise reduction, and output corresponding statistical information. Exemplary statistical information may include statistical data related to automatic exposure (AE), automatic white balance (AWB), automatic focus (AF), or any combination thereof for the current frame image. More information on acquiring the optical flow velocity of each pixel among multiple pixels in the current frame image can be found in Figure 4 and its related description.

[0051] In 304, the processing device 120 (e.g., the determination module 220) can determine the current brightness and current shutter speed limit of the imaging device based on the optical flow velocity of each pixel.

[0052] Current brightness refers to the brightness of the image in the current frame.

[0053] In some embodiments, the processing device 120 may segment the current frame image into a high-speed motion region and a low-speed motion region based on the optical flow velocity of each pixel. The high-speed motion region includes a first pixel among a plurality of pixels, and the low-speed motion region includes a second pixel among a plurality of pixels. The processing device 120 may assign a first luminance weight and a second luminance weight to each first pixel and each second pixel, respectively. The processing device 120 may determine the current luminance based on the first luminance weight of the first pixel, the second luminance weight of the second pixel, and the initial luminance information of the current frame image.

[0054] A high-speed motion region refers to a region including first pixels with a higher optical flow velocity, while a low-speed motion region refers to a region including second pixels with a lower optical flow velocity. That is, the optical flow velocity (also called the first optical flow velocity) of the first pixel is greater than the optical flow velocity (also called the second optical flow velocity) of the second pixel. In some embodiments, the first optical flow velocity of each first pixel may be the same or different, and the second optical flow velocity of each second pixel may be the same or different; this is not limited here.

[0055] In some embodiments, the processing device 120 can segment the current frame image into high-speed motion regions and low-speed motion regions using a preset segmentation algorithm. Exemplary preset segmentation algorithms may include a global threshold algorithm, a segmentation algorithm based on optical flow clustering, a segmentation algorithm combining optical flow, a segmentation model combining optical flow, or any combination thereof. For example, the processing device 120 can obtain a velocity segmentation threshold, and then determine each pixel as a first pixel or a second pixel based on a comparison between the optical flow velocity threshold and the optical flow velocity of each pixel. That is, the processing device 120 can determine whether each pixel belongs to a high-speed motion region or a low-speed motion region based on the comparison result, thereby segmenting the current frame image into high-speed motion regions and low-speed motion regions. The velocity segmentation threshold can be preset or determined based on the optical flow velocity of each pixel in the current frame image.

[0056] As an example only, the segmentation of the current frame image into high-speed motion regions and low-speed motion regions using a global thresholding algorithm will be described. The processing device 120 can determine the average optical flow velocity of the current frame image based on the optical flow velocity of each pixel (all pixels or a subset of pixels) and the number of these pixels. Then, the processing device 120 can determine a velocity segmentation threshold for the current frame image based on the average optical flow velocity, and segment the current frame image into high-speed motion regions and low-speed motion regions based on the velocity segmentation threshold. For example, the processing device 120 may identify pixels in the current frame image whose optical flow velocity is greater than the velocity segmentation threshold as first pixels, and define the region containing the first pixel as a high-speed motion region. Similarly, the processing device 120 may identify pixels in the current frame image whose optical flow velocity is less than or equal to the velocity segmentation threshold as second pixels, and define the region containing the second pixel as a low-speed motion region. In some embodiments, the average optical flow velocity can also be a median, weighted value, or other mathematical statistical value, which is not limited here.

[0057] In some embodiments, the processing device 120 may use a global thresholding algorithm to determine the velocity segmentation threshold of the current frame image. Other thresholding algorithms (e.g., Otsu's method, percentage thresholding algorithm, adaptive thresholding algorithm, iterative thresholding algorithm, etc.) may also be used, and are not limited here. For example, the processing device 120 may calculate the average optical flow velocity of each pixel in a plurality of pixels of the current frame image to obtain an initial velocity segmentation threshold for the current frame image; segment the current frame image according to the initial velocity segmentation threshold to obtain a first region and a second region in the current frame image; calculate the average optical flow velocity of the first region and the average optical flow velocity of the second region to obtain a current velocity segmentation threshold. If the difference between the initial velocity segmentation threshold and the current velocity segmentation threshold is less than a preset threshold difference, the current velocity segmentation threshold is determined as the velocity segmentation threshold. If the difference between the initial velocity segmentation threshold and the current velocity segmentation threshold is greater than the preset threshold difference, the current velocity segmentation threshold is used as a new initial velocity segmentation threshold, and the above steps are repeated. The preset threshold difference may be set manually or automatically by the system, and is not limited here.

[0058] As an example, the process of determining the velocity segmentation threshold of the current frame image using a global thresholding algorithm is described. The processing device 120 can determine the initial velocity segmentation threshold T0 of the current frame image by the average optical flow velocity of the current frame image. Then, the processing device 120 can segment the current frame image into an initial high-speed region (i.e., the first region) and an initial low-speed region (i.e., the second region) according to the initial velocity segmentation threshold T0. The processing device 120 can determine the average optical flow velocity m1 of the first region according to the optical flow velocity of each pixel in the first region and the number of each pixel in the first region; and determine the average optical flow velocity m2 of the second region according to the optical flow velocity of each pixel in the second region and the number of each pixel in the second region. The processing device 120 can also calculate the average of the average optical flow velocities of the first region and the second region based on m1 and m2, i.e., the current velocity segmentation threshold T1. The current velocity segmentation threshold T1 can be determined by formula (1):

[0059] If the difference between the current velocity segmentation threshold T1 and the initial velocity segmentation threshold T0 is less than the preset threshold difference, then the current velocity segmentation threshold T1 can be determined as the velocity segmentation threshold T, and the current frame image can be segmented into a high-speed motion region and a low-speed motion region according to the velocity segmentation threshold T.

[0060] If the difference between the current velocity segmentation threshold T1 and the initial velocity segmentation threshold T0 is greater than or equal to a preset threshold difference, then according to the principle of iterative calculation, the current velocity segmentation threshold T1 can be used as the new initial velocity segmentation threshold T0 for the next iteration. That is, T1 is assigned to T0, making T0 equal to T1. Referring to the aforementioned steps, a new current velocity segmentation threshold T1 is determined. Specifically, the processing device 120 segments the current frame image into a new first region and a new second region based on the new initial velocity segmentation threshold T0, and determines the average optical flow velocity m1 of the new first region and the average optical flow velocity m2 of the new second region. The processing device 120 then calculates the average of m1 and m2 to obtain the new current velocity segmentation threshold T1. The processing device 120 can continue to compare the new initial velocity segmentation threshold T0 and the new current velocity segmentation threshold T1 to determine the velocity segmentation threshold T, or continue iterating and repeating the above operations until the difference between two consecutive current velocity segmentation thresholds in adjacent iterations is less than a preset threshold difference.

[0061] By using a global thresholding algorithm, the optical flow velocity of the current frame image can be iteratively processed to obtain the optimal velocity segmentation threshold, thereby accurately segmenting the current frame image into high-speed and low-speed regions, and thus accurately allocating brightness weights and determining target brightness.

[0062] In some embodiments, the processing device 120 may assign a first luminance weight and a second luminance weight to each first pixel and each second pixel, respectively.

[0063] A luminance weight is a numerical coefficient related to luminance. Luminance weights can be used to adjust the influence of luminance information from different pixels (or image blocks) on the calculation of the current luminance. For example, a first pixel and a second pixel can be assigned different first luminance weights and second luminance weights. As an example only, the first luminance weight can be less than the second luminance weight. In some embodiments, the first luminance weight of each first pixel can be the same or different. For example, the first luminance weight of each first pixel can be 0.1; or 0.2; or 0.3; or 0.4; or 0.5; or 0.6; or 0.8, etc. As another example, the processing device 120 can pre-set a first correspondence (e.g., a correspondence table, correspondence function, correspondence model, etc.) between multiple candidate first optical flow velocities and multiple candidate first luminance weights, and determine the first luminance weight corresponding to each first pixel based on the first optical flow velocity and the first correspondence. Similarly, the second luminance weight of each second pixel can be the same or different. For example, the second luminance weight of each second pixel may be 1.0; or all may be 1.1; or all may be 1.2; or all may be 1.4; or all may be 1.5; or all may be 1.6; or all may be 2.0; or all may be 2.5; or all may be 3.0, etc. As another example, the processing device 120 may pre-set a second correspondence (e.g., a correspondence table, correspondence function, correspondence model, etc.) between multiple candidate second optical flow velocities and multiple candidate second luminance weights, and determine the second luminance weight corresponding to each second pixel based on the second optical flow velocity and the second correspondence of each second pixel.

[0064] In some embodiments, the processing device 120 may determine the current brightness based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image.

[0065] Initial brightness information refers to the statistical information of the unweighted brightness components derived from the original image data. Initial brightness information can include the initial brightness information of each pixel (all pixels or a subset of pixels) in the current frame image.

[0066] In some embodiments, the processing device 120 can determine the RGBY information of each pixel based on the pixel information of each pixel in the current frame image. R refers to red, G to green, B to blue, and Y to luminance. The processing device 120 can determine the RGBY statistical value (R0) by statistically analyzing the RGBY information of all pixels in the current frame image. 统计 G 统计 B 统计 Y 统计 ), and Y 统计 Used as initial brightness information for the current frame image.

[0067] In some embodiments, the processing device 120 may weight the initial brightness information of the current frame image based on a first brightness weight of a first pixel and a second brightness weight of a second pixel to determine the current brightness. As an example only, the processing device 120 may determine weighted brightness information for each first pixel based on the first brightness weight of each first pixel and the initial brightness information of each first pixel, and determine weighted brightness information for each second pixel based on the second brightness weight of each second pixel and the initial brightness information of each second pixel. The processing device 120 may calculate the average of the weighted brightness information of each first pixel and the weighted brightness information of each second pixel to obtain an average brightness value, and determine the average brightness value as the current brightness.

[0068] In some embodiments, the processing device 120 may pre-configure multiple brightness weights. Different brightness weights may correspond to different pixel information. Exemplary brightness weights may include preset motion weights, brightness motion weights, brightness filtering weights, etc., or any combination thereof.

[0069] Preset motion weights refer to empirical constants determined based on historical data or application scenarios. For example, preset motion weights may include high-speed motion weights q. 高 and low-speed motion weight q 低 High-speed motion weight q 高 It can be less than the weight q of low-speed motion. 低 For example, the high-speed motion weight q 高 It can be less than 1; the lower the value, the smaller the first brightness weight of the first pixel in a high-speed motion region. The weight q for low-speed motion... 低 It can be set to 1.0. For example, the weight q for high-speed motion... 高 and low-speed motion weight q 低 The method for determining the first luminance weight can be the same as or similar to the method for determining the second luminance weight.

[0070] Luminance motion weights refer to luminance weights associated with motion (e.g., optical flow velocity). Luminance motion weights can be used to highlight or suppress specific moving regions when determining the current luminance of a single frame of an image. For example, processing device 120 can assign a corresponding luminance motion weight to each pixel in the current frame image based on its optical flow velocity.

[0071] As an example only, the brightness motion weights are described using a first brightness weight and a second brightness weight. The processing device 120 can determine the mean of the first optical flow velocity in the high-speed motion region based on the first optical flow velocity of each first pixel in the high-speed motion region. u高Furthermore, the mean value of the second optical flow velocity in the low-speed motion region is determined based on the second optical flow velocity of each second pixel in the low-speed motion region. u低 .

[0072] The processing device 120 can traverse each pixel in a plurality of pixels of the current frame image. If the optical flow velocity of a pixel... u Greater than the mean of the first optical flow velocity u高 Then the high-speed motion weight q can be... 高 The brightness motion weight q of the pixel is determined. 运动 If the optical flow velocity of a pixel is flow u Less than the mean of the second optical flow velocity u低 Then the weight q for low-speed motion can be adjusted. 低 The brightness motion weight q of the pixel is determined. 运动 If the optical flow velocity of a pixel is flow u First optical flow velocity mean u高 Second optical flow velocity mean u低 Between these, the high-speed motion weight q can be... 高 and low-speed motion weight q 低 The brightness motion weight q is obtained by performing a weighted summation process. 运动 As an example only, the brightness motion weight q for each pixel out of multiple pixels in the current frame image. 运动 It can be determined according to formula (2): Where, ratio 运动 It can be used to weight q for high-speed motion 高 and low-speed motion weight q 低 The weights used for weighted summation can be pre-set weights, or they can be based on the optical flow velocity of the pixels and the mean of the first optical flow velocity. u高 and the mean of the second optical flow velocity u低 Certainly, but not specifically. For example, ratio. 运动 It can be determined according to formula (3):

[0073] Furthermore, the processing device 120 can determine the brightness motion weight q of each pixel among multiple pixels in the current frame image. 运动 and the initial brightness information Y of the current frame image 统计 Determine the current brightness of the current frame image. As an example only, the current brightness of the current frame image can be determined using formula (4): Among them, Y current It can be the current brightness, or average brightness. y It is the average brightness of the current frame image.

[0074] At this time, the processing device 120 will set the average brightness (avg) of the current frame image. y The current brightness Y of the current frame image current .

[0075] By introducing a brightness motion weight q 运动 This can increase the weight of stationary regions in the current frame image, thereby biasing the average brightness of the current frame image towards the brightness of stationary regions, and thus improving the brightness stability of the current frame image.

[0076] Luminance filtering weights refer to luminance weights associated with filtering processing. In some embodiments, luminance filtering weights can be used to determine the proportion of the initial current luminance of the current frame image in the multiple consecutive frame images when performing filtering operations on multiple consecutive frame images (e.g., multiple frame images in a time series). For example, processing device 120 can determine the proportion of the initial current luminance of the current frame image in the multiple consecutive frame images based on the overall motion of the current frame image (e.g., the mean of a first optical flow velocity). u高 and the mean of the second optical flow velocity u低 The brightness filtering weights of the current frame image are determined. For example, the processing device 120 can determine the initial current brightness (i.e., set the average brightness (avg) of the current frame image based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image. y (As the initial current brightness of the current frame image). The processing device 120 can determine the brightness filtering weight of the current frame image based on preset filtering parameters, and then perform filtering processing on the current frame image based on the brightness filtering weight and the initial current brightness to determine the current brightness. The current brightness determined in this way can also be called the filtered brightness.

[0077] Exemplary filtering processes may include Motion-Compensated Temporal Filtering (MCTF), Motion-Compensated Spatio-Temporal Filtering (MCSTF), Filtering Along Motion Trajectories, Motion-Adaptive Filtering, or any combination thereof. In some embodiments, filtering processes can be used to extract information, remove noise, enhance features, and smooth images from multiple consecutive frames (including the current frame). For example, filtering can be performed using a one-dimensional weighted average filter (mean filter) applied to the time series.

[0078] Preset filtering parameters can be used to determine the overall motion of the current frame image. For example, preset filtering parameters can include high-speed motion speed u. max and low-speed motion speed u min High-speed motion speed u max and low-speed motion speed u min It can be used to determine the overall motion of the current frame image (e.g., high-speed motion, low-speed motion / stationary, or medium-speed motion in between). For example, when the mean of the first optical flow velocity of the current frame image... u高 (or the mean of the second optical flow velocity) u低 (greater than high-speed motion speed u) max At that time, the overall motion of the current frame image is high-speed motion. When the mean of the first optical flow velocity of the current frame image is... u高 (or the mean of the second optical flow velocity) u低 (less than the low-speed motion speed u) min At that time, the overall motion of the current frame image is low-speed motion / stationary. When the mean of the first optical flow velocity of the current frame image is... u高 (or the mean of the second optical flow velocity) u Low) speed less than or equal to high speed u max And greater than or equal to the low-speed motion speed u min At that time, the overall motion of the current frame image is medium speed.

[0079] For example, preset filtering parameters may include the maximum value q of the brightness filtering weight. max and minimum value q min High-speed motion speed u max Low-speed motion speed u min The maximum value q of the brightness filter weights max and minimum value q min It can be set manually or determined automatically by the system.

[0080] As an example only, the mean of the first optical flow velocity is used. u高 Taking the determination of the overall motion of the current frame image as an example, let's illustrate this. When the mean of the first optical flow velocity... u高 Greater than the high speed of motion u max At that time, the overall motion of the current frame image is high-speed motion, and the processing device 120 can reduce the minimum value q. min The brightness filtering weight q of the current frame image is determined. 滤波 When the mean of the first optical flow velocity is... u高 Less than the low speed of motion u min At that time, the overall motion of the current frame image is low-speed motion / stationary, and the processing device 120 can convert the maximum value q maxThe brightness filtering weight q of the current frame image is determined. 滤波 When the mean of the first optical flow velocity is... u高 Less than or equal to the high speed u max And greater than or equal to the low-speed motion speed u min At that time, the overall motion of the current frame image is medium speed, and the processing device 120 can convert the maximum value q max and minimum value q min Perform weighted summation to obtain the brightness filtering weight q of the current frame image. 滤波 As an example only, the brightness filtering weight q of the current frame image. 滤波 It can be determined according to formula (5): Where, ratio 滤波 It can be used to set the maximum value q max and minimum value q min The weights used for weighted summation can be pre-set weights or based on the mean of the first optical flow velocity of the current frame image. u高 High-speed motion speed u max and low-speed motion u min Certainly, but not specifically. For example, ratio. 滤波 It can be determined according to formula (6):

[0081] Furthermore, the processing device 120 can determine the initial current brightness (e.g., average brightness avg) of the current frame image. y ) and brightness filter weight q 滤波 Filtering is performed to determine the current brightness of the current frame image. As an example only, the current brightness of the current frame image can be determined according to formula (7): Among them, avg y滤波 This represents the filtered brightness of the current frame image, where i is the sequence number of each frame in a series of consecutive frames, and avg... yi q is the average brightness of the i-th frame in a series of consecutive frames. 滤波i It is the brightness filtering weight of the i-th frame in a multi-frame continuous image.

[0082] At this time, the processing device 120 will adjust the filtered brightness (avg) of the current frame image. y滤波 The current brightness Y of the current frame image current .

[0083] By combining optical flow velocity in the spatial dimension (single-frame image) and filtering operations in the temporal dimension (multi-frame image), it is possible to intelligently distinguish between stationary objects (i.e., background environment, such as roads, buildings, trees, etc.) and moving objects (i.e., target objects, such as vehicles) in the current scene, thereby determining accurate and stable current brightness in a dynamically changing environment, thus improving the robustness of subsequent exposure control and image quality.

[0084] The current shutter speed limit refers to the fastest shutter speed (i.e., the shortest exposure time) allowed by the imaging device, determined based on the optical flow velocity of the current frame image.

[0085] In some embodiments, the processing device 120 can determine the average optical flow velocity (i.e., the mean of the first optical flow velocity) of the high-speed motion region based on the optical flow velocity of each first pixel in the high-speed motion region. u高 The current shutter speed limit is determined based on the average optical flow velocity. For example, the current shutter speed limit can be inversely proportional to the average optical flow velocity. That is, the faster the average optical flow velocity, the faster the current shutter speed limit (the shorter the exposure time). As an example only, the processing device 120 can determine the current shutter speed limit according to formula (8): Where, exp max It can be the current shutter speed limit, and 'a' can be an empirical constant (for example, the empirical constant 'a' is the maximum displacement of a moving object within the current shutter speed limit), which is not limited here.

[0086] According to some embodiments of this specification, by determining the current shutter speed limit based on the average optical flow velocity, the slowest shutter speed can be proactively and proactively limited according to the speed of the moving object, effectively suppressing motion blur (e.g., ghosting).

[0087] In some embodiments, the processing device 120 may further filter the average optical flow velocity of the high-speed moving region to obtain a filtered average optical flow velocity. For example, the processing device 120 may use a filter to filter multiple average optical flow velocities corresponding to consecutive frame images containing the current frame image to obtain a filtered average optical flow velocity. The filtering of multiple average optical flow velocities may be similar to the filtering of the average brightness of consecutive frame images described above, and is not limited here. As an example only, the current shutter speed limit may also be determined according to formula (9): Among them, u 滤波 The average optical flow velocity after filtering.

[0088] According to some embodiments of this specification, by filtering the average optical flow velocity, abrupt changes and noise in the average optical flow velocity can be eliminated, resulting in a filtered velocity (i.e., the filtered average optical flow velocity) that better reflects the true motion trend and exhibits smoother changes. This improves the stability, reliability, and accuracy of subsequent operations based on the filtered velocity, avoids overreaction in the image acquisition system, and ensures the shooting effect (e.g., the visual stability of the output video).

[0089] In some embodiments, the processing device 120 can adjust the current shutter speed limit based on the operating mode of the imaging device. The operating mode of the imaging device may include, but is not limited to, a first mode and a second mode. The first mode refers to the mode suitable for the imaging device to operate in a well-lit environment (the ambient brightness is considered sufficient when it is greater than a preset brightness threshold, and can also be called daytime mode). The second mode refers to the mode suitable for the imaging device to operate in a poorly lit environment (the ambient brightness is considered insufficient when it is less than or equal to a preset brightness threshold, and can also be called nighttime mode).

[0090] In some embodiments, the processing device 120 can determine the operating mode of the imaging device based on the current scene. For example, when the current scene is a daytime scene, the processing device 120 can determine that the operating mode of the imaging device is a first mode (i.e., daytime mode). When the current scene is a nighttime scene, the processing device 120 can determine that the operating mode of the imaging device is a second mode (i.e., nighttime mode).

[0091] In some embodiments, the processing device 120 may determine the operating mode of the imaging device based on a preset determination algorithm. For example, the processing device 120 may determine whether the imaging device is in night mode based on the preset determination algorithm. As an example only, the preset determination algorithm may be a brightness-based determination algorithm, and the processing device 120 may use the preset determination algorithm based on the current brightness (e.g., average brightness avg) of the current frame image. y Filter brightness avg y滤波 Determine if the imaging device is in night mode.

[0092] For example, the processing device 120 can determine the ambient brightness (or the ambient brightness of the imaging device) corresponding to the current frame image based on the operating parameters of the imaging device corresponding to the current frame image (e.g., aperture parameters, shutter speed, ISO, exposure gain, etc.). As an example only, the processing device 120 can determine the brightness multiplier X of the current frame image based on the shutter speed (also known as the current shutter speed) (in lines) and the exposure gain (also known as the current exposure gain) (in decibels (dB)) corresponding to the current frame image. For example, the brightness multiplier X of the current frame image can be determined according to formula (10):

[0093] Furthermore, the processing device 120 can determine the ambient brightness lum of the current frame image based on the current brightness and brightness multiplier X of the current frame image. For example, if the current brightness is the filtered brightness avg... y After filtering, the ambient brightness lum of the current frame image can be determined according to formula (11):

[0094] In some embodiments, the processing device 120 can directly obtain the operating parameters of the imaging device from the imaging device, or it can retrieve the operating parameters of the imaging device from the storage device storing the operating parameters of the imaging device.

[0095] In some embodiments, the processing device 120 can determine the exposure gain corresponding to the current frame image based on the aperture parameters of the imaging device. As an example only, if the imaging device uses an automatic aperture lens, since the aperture size affects the image's exposure, the processing device 120 can determine the change in light intake at the current aperture through a pre-calibration method or the relationship between the lens step size and the light-transmitting area, which can be equivalent to the exposure gain.

[0096] In some embodiments, the preset determination algorithm includes at least one determination threshold. For example, at least one determination threshold may include a preset brightness threshold. The processing device 120 can compare the ambient brightness lum with the preset brightness threshold to determine the operating mode of the imaging device. For example, when the ambient brightness lum is greater than or equal to the preset brightness threshold, the processing device 120 can determine that the current ambient brightness is sufficient and determine that the operating mode of the imaging device is a first mode. As another example, when the ambient brightness lum is less than the preset brightness threshold, the processing device 120 can determine that the current ambient brightness is insufficient and determine that the operating mode of the imaging device is a second mode.

[0097] In some embodiments, the processing device 120 can determine the operating mode of the imaging device based on the ambient brightness lum of multiple consecutive or non-consecutive frames acquired by the imaging device. For example, when the ambient brightness lum of multiple frames is greater than or equal to a preset brightness threshold, or when the ambient brightness lum of a certain number of frames in the multiple frames is greater than or equal to a preset brightness threshold (a certain number of frames is greater than a preset number threshold), the processing device 120 can determine that the current ambient brightness is sufficient and determine the operating mode of the imaging device as a first mode. As another example, when the ambient brightness lum of multiple frames is less than a preset brightness threshold, or when the ambient brightness lum of a certain number of frames in the multiple frames is less than a preset brightness threshold, the processing device 120 can determine that the current ambient brightness is insufficient and determine the operating mode of the imaging device as a second mode.

[0098] The preset brightness threshold may include a single brightness threshold or multiple brightness thresholds. For example, the preset brightness threshold may include b1 and b2 (b1 is less than b2, and the specific value is not limited). If the ambient brightness lum of multiple frames is greater than or equal to b2, or if the ambient brightness lum of a certain number of frames is greater than or equal to b2, the processing device 120 can determine that the current ambient brightness is sufficient and determine that the imaging device operates in the first mode. Alternatively, if the ambient brightness lum of multiple frames is less than b1, or if the ambient brightness lum of a certain number of frames is less than b1, the processing device 120 can determine that the current ambient brightness is insufficient and determine that the imaging device operates in the second mode.

[0099] In some embodiments, the processing device 120 can switch the operating mode of the imaging device. For example, if the imaging device is operating in the second mode, and the ambient brightness lum of multiple frames is greater than or equal to b2, or the ambient brightness lum of a certain number of frames is greater than or equal to b2, the processing device 120 can switch the operating mode of the imaging device to the first mode. Otherwise, the processing device 120 can maintain the operating mode of the imaging device in the second mode, reset the count of multiple frames to zero, and re-count the number of frames. As another example, if the imaging device is operating in the first mode, and the ambient brightness lum of multiple frames is less than b1, or the ambient brightness lum of a certain number of frames is less than b1, the processing device 120 can switch the operating mode of the imaging device to the second mode. Otherwise, the processing device 120 can maintain the operating mode of the imaging device in the first mode, reset the count of multiple frames to zero, and re-count the number of frames.

[0100] In some embodiments, at least one determination threshold in the preset determination algorithm can be determined based on the environment in which the imaging device is located or the current scene. For example, at least one determination threshold can be determined based on environmental features (or scene features). Environmental features (or scene features) may include weather type (sunny, rainy), imaging device installation angle, road type (open road, tunnel, expressway, urban road, rural road, etc.), or any combination thereof. As an example only, the processing device 120 can construct a determination threshold database based on historical data from different imaging devices. Specifically, the processing device 120 can acquire multiple historical environmental features from the historical data and multiple sets of historical determination thresholds b1 and b2 corresponding to each historical environmental feature. For each historical environmental feature, the processing device 120 can determine the historical determination thresholds b1 and b2 with the highest accuracy as the preferred determination thresholds b1 and b2 corresponding to that historical environmental feature. The determination threshold database includes multiple historical environmental features and corresponding preferred determination thresholds b1 and b2. The processing device 120 can determine the corresponding preferred determination thresholds b1 and b2 from the determination threshold database according to the environmental features of the environment in which the imaging device is located, as determination thresholds b1 and b2 that match the current scene.

[0101] By constructing a judgment threshold database, the judgment threshold can be automatically determined based on the environmental characteristics of the imaging device's environment. This allows the imaging device to automatically adapt to the current lighting conditions and seasonal changes, reducing manual debugging costs and improving the product's versatility.

[0102] In some embodiments, after determining the operating mode, the processing device 120 can adjust the current shutter speed limit according to the operating mode of the imaging device. For example, when the operating mode of the imaging device is a first mode, the processing device 120 can decrease the current shutter speed limit (i.e., shorten the exposure time). When the operating mode of the imaging device is a second mode, the processing device 120 can increase the current shutter speed limit (i.e., extend the exposure time).

[0103] In some embodiments, a target light source may also be provided in the current scene. The target light source is a controllable light source used to provide supplemental lighting to the current scene. For example, when the imaging device is in its first operating mode, the target light source may be in a dormant state and not provide supplemental lighting for the imaging device. When the imaging device is in its second operating mode, the target light source may be activated to provide supplemental lighting for the imaging device.

[0104] The target light source can be a supplementary light. Exemplary supplementary lights may include strobe lights (e.g., controllable strobe lights), constant-on supplementary lights, infrared supplementary lights, white light supplementary lights, flashing supplementary lights, hybrid supplementary lights, etc., or any combination thereof. Taking an infrared supplementary light application scenario as an example, when the imaging device is in its first operating mode, it can acquire color images. When the imaging device is in its second operating mode, it can acquire grayscale images.

[0105] In some embodiments, the target light source and the imaging device can be integrated or separate. In some embodiments, the target light source can be communicatively connected to the imaging device. Alternatively, the target light source may not be communicatively connected to the imaging device.

[0106] In some embodiments, when the imaging device operates in the second mode, the processing device 120 can determine whether a target light source exists in the environment where the imaging device is located. For example, the user can preset whether a target light source exists in the environment where the imaging device is located. Alternatively, the processing device 120 can determine the presence of a target light source based on real-time acquired attribute information of the target light source.

[0107] If the imaging device is in the second mode (i.e., night mode) and there is a target light source (e.g., a controllable strobe light) in the environment where the imaging device is located, the processing device 120 can adjust the current shutter speed limit based on the current shutter speed limit and the adjustment coefficient corresponding to the target light source.

[0108] The adjustment factor can be preset according to the target light source. For example, different target light sources can correspond to the same or different adjustment factors. Alternatively, the adjustment factor can be a multiplication factor greater than 1, used to relax the current shutter speed limit. In some embodiments, the adjustment factor can be set manually or determined automatically by the system.

[0109] This explanation uses a controllable strobe light as an example. A controllable strobe light is a fill light whose brightness, trigger frequency, and flash pulse width can be controlled. For example, the adjustment factor can range from 1 to 5. Another example is a range from 1 to 3. Yet another example is a range from 1 to 2. And yet another example is a range from 1 to 1.5.

[0110] In some scenarios (e.g., road detection scenarios), the ambient light (e.g., streetlights) is weak, and because vehicle license plates are placed vertically, they reflect light and are difficult for imaging devices to detect. Therefore, the main supplementary lighting for the vehicle comes from the controllable strobe lamp. Thus, it can be approximated that the license plate's supplementary lighting time is controlled by the pulse width of the controllable strobe lamp (the strobe lamp pulse width is generally less than the shutter speed limit). The shutter speed limit of the imaging device can be appropriately increased to improve the overall image brightness without causing the license plate to appear as a blur. The increase ratio is the adjustment coefficient, but if the adjustment coefficient is too large, it will still cause vehicle body blur. Therefore, the adjustment coefficient can be set within a certain range based on experience (specifically determined according to the actual application scenario). Therefore, if the imaging device is in the second mode (i.e., night mode) and there is a controllable strobe lamp in the environment where the imaging device is located, the processing device 120 can determine the current shutter speed limit based on the pulse width and adjustment coefficient of the controllable strobe lamp. For example, the current shutter speed limit can also be determined according to formula (12): exp max =c×t, (12) where c can be an adjustment coefficient and t can be the pulse width of the controllable strobe lamp.

[0111] It should also be noted that the operating parameters of the imaging device in daytime mode can be the same or different from those in nighttime mode. For example, when the imaging device is operating in daytime mode, due to sufficient brightness, its operating parameters (e.g., aperture, shutter speed, ISO, exposure gain, etc.) can be lower than those in nighttime mode to avoid overexposure in daytime scenes. Therefore, when controlling the imaging device to operate in different modes, in addition to controlling its corresponding fill light, its corresponding operating parameters can also be adjusted.

[0112] In 306, the processing device 120 (e.g., the determination module 220) can determine the target exposure parameters of the imaging device based on the current brightness and the current shutter speed limit.

[0113] Target exposure parameters refer to the parameter values ​​that an imaging device (e.g., the image sensor of the imaging device) needs to set or adjust in order to achieve the target brightness. Target exposure parameters can include target shutter speed, target exposure gain, or any combination thereof. Taking target shutter speed as an example, it can be the shutter speed value that the imaging device needs to set or the shutter speed value that needs to be adjusted.

[0114] Target brightness refers to the image brightness that the imaging device (e.g., the automatic exposure system of the imaging device) expects to achieve (i.e., the image brightness that needs to be adjusted). In some embodiments, the target brightness can be manually set or automatically determined by the system.

[0115] In some embodiments, the processing device 120 may determine multiple exposure segments based on the current shutter speed limit, and determine a target exposure gain equivalent multiple based on the target brightness and the current brightness. Further, the processing device 120 may determine a target exposure segment from the multiple exposure segments based on the target exposure gain equivalent multiple, and determine target exposure parameters based on the target exposure segment. The target exposure segment refers to the exposure segment in which the target exposure gain equivalent multiple is located among the multiple exposure segments.

[0116] In some embodiments, the processing device 120 may perform interval segmentation processing on the exposure interval based on the current shutter speed limit to obtain multiple exposure segments (also referred to as exposure sub-intervals).

[0117] An exposure range refers to the range of exposure errors that an imaging device can tolerate while preserving usable image details. The processing device 120 can pre-set the exposure range of the imaging device (e.g., the image sensor of the imaging device) based on parameter information. For example, the exposure range of the imaging device can be a range consisting of an upper and lower limit of the exposure time. As an example only, the lower limit of the exposure time can be the number of lower exposure rows (e.g., 1 row) of the image sensor in non-long exposure mode, and the upper limit of the exposure time is the number of upper exposure rows of the image sensor in non-long exposure mode. Within each exposure segment, the exposure effect of the image can be adjusted by adjusting the shutter speed or exposure gain.

[0118] For ease of explanation, this specification uses the number of exposure rows as an example to represent exposure time. For example, the exposure range can be [1, vmax], which means that the number of exposure rows of the imaging device can be adjusted between the lower limit of the exposure rows (e.g., 1 row) and the upper limit of the exposure rows (e.g., vmax rows), thereby adjusting the image exposure effect.

[0119] During exposure adjustment, the typical strategy is to adjust the number of exposure rows of the imaging device from 1 row to vmax row (referred to as exposure segment A). If further exposure adjustment is needed after the number of exposure rows reaches vmax row, the exposure gain can be changed to achieve the desired effect (referred to as exposure segment B). Similarly, a preset exposure gain range can be pre-set, such as [0, 100], which means that after the number of exposure rows reaches vmax, the exposure gain of the imaging device can be adjusted from 0 dB to 100 dB, thus achieving further exposure adjustment. While this method effectively adjusts the exposure, increasing the exposure limit makes it difficult to avoid motion blur issues caused by fast-moving objects in the image.

[0120] To address the aforementioned issues, the processing device 120 can divide the exposure range [1, vmax] into exposure segments 1-3 based on the current shutter speed limit. Specifically, in exposure segment 1, the number of exposure rows is adjusted from 1 row to exp. max The exposure is adjusted in segment 1, while keeping the gain at 0dB (the exposure effect is changed by adjusting the number of exposure lines in this segment). If further exposure enhancement is needed, exposure segment 2 is used, in which case the number of exposure lines is kept at exp. max Okay, adjust the exposure gain from 0dB to 100dB (this segment changes the exposure effect by adjusting the exposure gain), which can avoid the motion blur problem of fast-moving objects caused by continuously increasing the number of exposure lines. If the exposure gain is 100dB and you still need to increase the exposure effect, use exposure segment 3. In this case, keep the exposure gain at 100dB and increase the number of exposure lines from exp max The line is adjusted to the vmax line (the exposure effect is changed by further adjusting the number of exposure lines in this segment).

[0121] It should be noted that when adjusting the number of exposure rows and / or the exposure gain in this specification, the adjustment can be made all at once, gradually according to the adjustment step size, or according to a preset linear relationship; no limitation is made here. Furthermore, the above embodiments are for illustrative purposes only, using the aforementioned exposure range and gain range as simple examples. In actual application scenarios, more preset exposure ranges and / or preset gain ranges can be included and adjusted as needed.

[0122] In some embodiments, each exposure segment may include two endpoints, left and right. Taking the right endpoint of each exposure segment (equivalent to the maximum value of each exposure segment) as an example, the equivalent gain multiple corresponding to each exposure segment can be determined based on the shutter speed (shut) and exposure gain (gain) corresponding to the right endpoint. For example, the equivalent gain multiple corresponding to each exposure segment can be determined according to formula (13): equal db = gain + 20 * log(shut), (13) where equal db This can be the equivalent gain factor. The base in the logarithm calculation can be 10, but this is omitted here for simplicity and will not be elaborated further. The equivalent gain factor refers to the value that equates shutter speed and exposure gain to the same unit.

[0123] In some embodiments, the processing device 120 may determine the equivalent gain to be adjusted based on the target brightness and the current brightness, and determine the target equivalent gain based on the current equivalent gain and the equivalent gain to be adjusted. The equivalent gain to be adjusted can be used to adjust the imaging device from the current brightness to the target brightness. For example, the target equivalent gain can be determined according to formula (14): aimdb =curr db +adj db (14) where aim db It can be the target gain equivalent multiple, curr db It can be the current gain equivalent multiple, adj. db It can be the equivalent multiple of the gain to be adjusted.

[0124] In some embodiments, the processing device 120 can determine the equivalent gain to be adjusted based on the ratio between the current brightness (e.g., filtered brightness) and the target brightness. The processing device 120 can acquire the current shutter speed and current exposure gain corresponding to the current frame image, and determine the current equivalent gain based on the current shutter speed and current exposure gain. Further, the processing device 120 can sum the equivalent gain to be adjusted and the current equivalent gain to obtain the target equivalent gain.

[0125] For example, the equivalent gain factor adj to be adjusted db It can be determined according to formula (15): Among them, aim y It can be the target brightness.

[0126] Optionally, to optimize image display quality and prevent oscillations during exposure adjustment, gradual adjustments can be made during the exposure adjustment process. For example, the initially determined equivalent gain multiple adj to be adjusted can be gradually increased each time. db The gain is reduced to an equivalent multiple (adj) to be adjusted. db And adjust accordingly. For example, you can adjust the gain by the equivalent multiple adj obtained each time. db Adjustments are made based on one-fifth of the original value, as shown in formula (16):

[0127] Then, the processing device 120 can obtain the current shutter speed corresponding to the current frame image. 当前 and current exposure gain 当前 (For example, obtained from an imaging device or storage device), and the current gain equivalent factor curr is determined based on formula (17). db The root can be: curr_db = gain 当前 +20*log(shut 当前 (17)

[0128] Finally, the processing device 120 can determine the target gain equivalent multiple aim according to formula (14). db .

[0129] In some embodiments, the processing device 120 may sequentially traverse the equivalent gain multiples (equal) corresponding to each exposure segment from smallest to largest. db If the equals of a certain exposure segment db Greater than aim db Then the current equal db The corresponding exposure segments satisfy aim db Exposure requirements. That is, the processing device 120 can determine this exposure segment as the target exposure segment.

[0130] It should be noted that increasing the gain of an imaging device may increase noise in the image, thus affecting image quality. Therefore, although equal... db Greater than aim db There can be one or more exposure segments, but the closest one to the aim can be determined by the sequential traversal method described above. db Exposure segments (i.e., during the traversal to equal) db Greater than aim db The time interval is used to obtain an image with good exposure and image quality. However, in practical applications, other equal intervals can also be selected. db Greater than aim db Exposure segmentation (which may affect image quality).

[0131] By re-segmenting the exposure range using the current shutter speed limit, motion blur issues caused by continuously increasing exposure lines can be avoided.

[0132] In some embodiments, the processing device 120 may determine the target exposure parameters based on the target exposure segments.

[0133] For example only, the exposure range is divided into exposure segments 1-3 as described above. In a scene with a gain range of [0, 100], if exposure segment 2 is the target exposure segment, then the aim can be determined. db The corresponding target shutter speed (aim) exp It should be fixed as exp max Then according to aim db The equivalent gain of the right endpoint of the adjacent previous exposure segment (exposure segment 1) is equal to db-last Then the target exposure gain (aim) can be determined. gain For example, target exposure gain (aim) gain Aim can be determined according to formula (18): gain =aim db -equal db - last (18)

[0134] In some embodiments, the processing device 120 can process target exposure parameters (e.g., target shutter speed, aim). exp and target exposure gain aim gain The signal is sent to the imaging device (e.g., an image sensor) and guides the imaging device to set the exposure parameters based on the target, so that the subsequently acquired images can meet the target requirements. db Exposure needs.

[0135] In some embodiments, the processing device 120 may also adjust parameters in the image signal processor of the imaging device. For example, the processing device 120 may adjust the noise reduction parameters in the image signal processor of the imaging device based on the filtered average optical flow velocity. Exemplary noise reduction parameters may include temporal intensity, spatial intensity, temporal filtering coefficients, edge preservation threshold, luminance dependence, etc., or any combination thereof. Temporal intensity can be used to control the mixing intensity between multiple consecutive frames of images. For example, by comparing the pixel differences between the current frame image and several previous frames, temporal averaging can be performed on stationary areas, while reducing mixing in moving areas to avoid motion blur. The greater the temporal intensity, the greater the influence of several previous frames on the current frame image, and the better the noise reduction effect on the current frame image (e.g., stationary areas). The smaller the temporal intensity, the smaller the influence of several previous frames on the current frame image, which can reduce motion blur, but the noise reduction effect is poor. Spatial intensity can be used to control the filtering intensity of a single frame image. For example, in the current frame image, the difference between a single pixel and its surrounding neighboring pixels is analyzed, and noise is smoothed by filtering while preserving edges. A higher spatial intensity results in a wider filtering range and greater strength, leading to better noise reduction but potentially causing detail blurring or texture loss. Conversely, a lower spatial intensity preserves more detail but results in poorer noise reduction. Temporal filtering coefficients are the coefficients set during time-dependent filtering. They control the mixing weights of the current frame and previous frames in temporal noise reduction (3DNR), reflecting the influence of previous frames on the current frame. The edge preservation threshold is the gradient threshold used to distinguish noise from real edges in spatial noise reduction; pixel changes exceeding this threshold are considered edges and preserved. Brightness dependence characterizes the adjustment rules for noise reduction intensity as pixel brightness changes. For example, low-brightness areas can enhance noise reduction, while high-brightness areas can weaken it.

[0136] By way of example only, the processing device 120 can be based on the empirical constant d1 of the corresponding time-domain intensity and the filtered average optical flow velocity u. 滤波 The temporal intensity is dynamically adjusted based on the empirical constant d2 of the corresponding spatial intensity and the filtered average optical flow velocity u. 滤波 Dynamically adjust airspace intensity.

[0137] To solve the motion blur problem, the time-domain parameters need to be reduced and the spatial-domain parameters need to be increased. Therefore, the adjusted time-domain parameters can be determined according to formula (19): Among them, Strength 时域 It can be the adjusted time-domain parameters. It can be a reference value for time-domain parameters.

[0138] The adjusted spatial parameters can be determined according to formula (20): Among them, Strength 空域 It can be the adjusted spatial parameters. It can be a baseline value for spatial parameters.

[0139] In some embodiments, the empirical constants d1 and d2 may be the same or different.

[0140] In some embodiments of this specification, the noise reduction parameters are dynamically adjusted using the filtered average optical flow velocity, which effectively suppresses motion blur. For example, when the current scene is static or the object is moving slowly, the processing device 120 can automatically increase the noise reduction intensity to obtain a high-quality image. When the object is moving at high speed, the processing device 120 can automatically reduce the noise reduction intensity, especially the temporal noise reduction portion, to avoid motion artifacts and prioritize ensuring the clear outline of the moving object. This adaptive noise reduction strategy enables the imaging device to achieve the best balance between noise suppression and detail preservation when capturing objects in any state of motion.

[0141] For example, the processing device 120 can adjust the operating mode of the imaging device based on the filtered average optical flow velocity. For instance, the processing device 120 can acquire the current motion mode of the imaging device, and based on the filtered average optical flow velocity and the current motion mode, determine the target motion mode of the imaging device from multiple motion modes using a mode switching algorithm, and switch the current motion mode of the imaging device to the target motion mode. More details on determining the target motion mode of the imaging device can be found in Figure 6 and its related description.

[0142] According to some embodiments of this specification, the current brightness and current shutter speed limit of the imaging device can be determined based on the optical flow velocity of each pixel in a current frame image; and the target exposure parameters of the imaging device can be determined based on the current brightness and current shutter speed limit. This enables the imaging device to ensure image sharpness while meeting the target brightness. Furthermore, since the current brightness and current shutter speed limit of the imaging device are determined based on the optical flow velocity of the pixels, exposure adjustment based on motion judgment is achieved, avoiding interference from high-speed moving objects in the exposure adjustment process and optimizing the effect of automatic exposure adjustment. This allows the imaging device to ensure appropriate brightness of the subject environment and clear outlines of moving objects in high-speed motion scenes, improving the overall imaging quality and video usability in complex dynamic environments.

[0143] It should be noted that the above description of process 300 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art following the guidance of this specification. However, these changes and modifications do not depart from the scope of this specification. In some embodiments, process 300 may be accomplished by one or more additional operations not described and / or by omitting one or more of the operations discussed above.

[0144] Figure 4 is a flowchart illustrating an exemplary process 400 for acquiring the optical flow velocity of each pixel among a plurality of pixels in a current frame image, according to some embodiments of this specification. In some embodiments, process 400 may be executed by image acquisition system 100. For example, process 400 may be stored in a storage device (e.g., storage device 130) in the form of an instruction set (e.g., an application program). In some embodiments, processing device 120 (e.g., one or more modules shown in Figure 2) may execute the instruction set and accordingly instruct one or more components of image acquisition system 100 to execute process 400. In some embodiments, the optical flow velocity of each pixel among a plurality of pixels in a current frame image, as described by operation 302 in Figure 3, may be acquired according to process 400.

[0145] In some embodiments, the processing device 120 may use optical flow to obtain the optical flow velocity of each pixel among a plurality of pixels in the current frame image.

[0146] As an example only, the optical flow method is used to illustrate the pyramid algorithm.

[0147] The processing device 120 can acquire raw image data and corresponding exposure information of two adjacent frames (e.g., the previous frame and the current frame).

[0148] The processing device 120 can determine the multiples X1 and X2 between the exposure values ​​of the previous frame image and the current frame image and the preset exposure value, respectively. For example, the multiples X1 or X2 can be determined according to formula (21): Among them, shut 预设 It can be the preset shutter speed corresponding to the preset exposure value, gain 预设 It can be the preset gain corresponding to the preset exposure value.

[0149] Processing device 120 can convert the raw image data of the two frames into statistical data to obtain RGBY statistical values ​​(i.e., R... 统计 G 统计 B 统计 Y 统计 (Four components). See Figure 5 for example only, which is an exemplary data conversion diagram illustrating some embodiments according to this specification. The original image data is represented by groups of four points, so each group of four points in Figure 5 generates a set of R... 统计 G 统计 B 统计 Y 统计 The final statistical values ​​are then calculated by dividing the resolution (width and height) by 2. For example, R... 统计 G 统计 B 统计 Y 统计 R can be determined according to formulas (22)-(25) respectively: 统计 =R-BLC_R, (22) B 统计 =B-BLC_B, (24) Y 统计 =(77*R) 统计 +150*G 统计 +29*B 统计 )>>8, (25) where BLC_R, BLC_Gr, BLC_Gb, and BLC_B are the black level values ​​corresponding to the original image data, which can generally be obtained through pre-calibration.

[0150] In some embodiments, the processing device 120 can perform exposure normalization processing on the current frame image and the previous frame image to generate second statistical information under equivalent exposure levels, and determine the optical flow velocity of each pixel in the current frame image based on the second statistical information. For example, the processing device 120 can use the Y-axis from the statistical information of the original image data of the two frames to determine the optical flow velocity of each pixel in the current frame image. 统计 The component is divided by its corresponding multipliers X1 and X2 to obtain a second statistical information at the equivalent exposure level. That is, R 统计 G 统计 B 统计The three components remain unchanged, only for Y 统计 The components are processed. For example, processing device 120 can process the R values ​​from the statistical information of the raw image data of two consecutive frames. 统计 G 统计 B 统计 Y 统计 The four components are divided by their corresponding multipliers X1 and X2 to obtain the second statistical information at the equivalent exposure level. That is, simultaneously, R... 统计 G 统计 B 统计 Y 统计 Four components are processed. The second statistical information of the previous frame image includes R. 统计1 G 统计1 B 统计1 Y 统计1 The second statistical information of the current frame image includes R 统计2 G 统计2 B 统计2 Y 统计2 .

[0151] The processing device 120 can reduce the second statistical information of the previous frame image and the second statistical information of the current frame image by m times according to a ratio k, to obtain m-level pyramid data. Here, k and m are empirical values ​​that can be set according to the resolution of the original statistical information.

[0152] Processing device 120 can traverse all layers of the pyramid and obtain statistical information Y for the nth layer. 统计1n and Y 统计2n Mean filtering is performed to reduce noise, and the filtered statistical information Y is obtained. 1n and Y 2n The mean filtering process can utilize a convolution function.

[0153] Processing device 120 can use a preset gradient function to process the filtered statistical information Y. 1n and Y 2n Extract the gradients in the x and y directions respectively to obtain the gradient fx in the x direction. 1n and fx 2n gradient fy in the y-direction 1n and fy 2n And obtain the averaged gradient fx in the x-direction. n and the gradient fy in the y direction n For example, fx n and fy n It can be determined according to formulas (26) and (27) respectively:

[0154] Processing device 120 can process the filtered statistical information Y 1n and Y2n The frame difference ft is obtained by subtraction. n For example, frame difference ft n It can be determined according to formula (28): ft n =Y 1n -Y 2n (28)

[0155] Processing device 120 can determine whether the currently traversed pyramid level is at the highest level of the pyramid (i.e., whether n equals m). If n equals m, processing device 120 can determine the optical flow velocity flow_x in the x and y directions of the current level. n初始 and flow_y n初始 The matrix is ​​initialized to all zeros. Optical flow velocity flow_x n初始 and flow_y n初始 The matrix includes the initial value of the optical flow velocity for each pixel in the current frame image.

[0156] If n is not equal to m, then use the flow_x calculated from the previous layer. n-1 and flow_y n-1 Multiply the matrix by the pyramid scaling factor k to get the flow_x of the current layer. n初始 and flow_y n初始 Initial values ​​for the matrix. For example, flow_x n初始 and flow_y n初始 The initial values ​​of the matrix can be determined according to formulas (29) and (30) respectively: flow_x n初始 =k*flow_x n-1 (29) flow_y n初始 =k*flow_y n-1 (30)

[0157] Processing device 120 can iteratively update the optical flow velocity flow_x in the x and y directions for each layer of the pyramid. n初始 and flow_y n初始 For example, for each layer of the pyramid (e.g., the current layer n), the processing device 120 can determine whether the current iteration meets the iteration termination condition. The iteration termination condition may include whether the previous iteration number i exceeds an iteration number threshold Tn (an empirical constant) and / or whether the optical flow velocity difference delta between adjacent iterations is less than a preset threshold Tm (an empirical constant that may vary with the resolution of the current pyramid). If the current iteration meets the iteration termination condition, the processing device 120 can exit the iteration and determine the optical flow velocity of each pixel of the multiple pixels in the current frame image.

[0158] If the current iteration does not meet the iteration termination condition, the processing device 120 can determine whether the current iteration is the first iteration of the current layer pyramid. If the current iteration is the first iteration of the current layer pyramid, the processing device 120 can maintain the optical flow velocity flow_x. n初始 and flow_y n初始 The matrix remains unchanged.

[0159] If the current iteration is not the first iteration of the current layer pyramid, the processing device 120 can retrieve the flow_x result of the previous iteration. n and flow_y n Assigning a matrix to flow_x n初始 and flow_y n初始 Matrix. Processing device 120 can process the optical flow velocity flow_x in the x and y directions. n初始 and flow_y n初始 The matrix is ​​subjected to mean filtering to obtain the filtered flow_x. n初始 and flow_y n初始 Matrix (i.e., flow_x) n 滤波 and flow_y n滤波 (Matrix). Processing device 120 can process the filtered flow_x using local averaging and optical flow constraint methods. n初始 and flow_y n初始 The matrix is ​​used to obtain the optical flow velocities flow_x in the x and y directions. n and flow_y n Matrix. For example, the optical flow velocities in the x and y directions, flow_x. n and flow_y n The matrix can be determined according to formulas (31)-(34): residuals1=fx n *(flow_x n滤波 -flow_x n初始 )+fy n *(flow_y n滤波 -flow_y n初始 )+ft n , (31) residuals2=lambda+fx n 2 +fy n 2 (32) Here, lambda is a preset compensation constant, which cannot be 0.

[0160] Processing device 120 can determine the optical flow velocity flow_x determined in the current iteration. n and flow_yn The matrix and the optical flow velocity flow_x determined in the previous iteration n初始 and flow_y n初始 The optical flow velocity difference delta between matrices. For example, the optical flow velocity difference delta can be determined according to formula (35): delta=(flow_x n -flow_x 初始 ) 2 +(flow_y n -flow_y 初始 ) 2 (35)

[0161] After determining the optical flow velocity difference delta, the processing device 120 can increment the iteration number i by 1 and determine whether the next iteration is needed.

[0162] After all layers of the pyramid have been iterated, the processing device 120 can determine the undirected optical flow velocity matrix flow_u based on the x and y direction optical flow velocities flow_x0 and flow_y0 matrices of the bottom-level pyramid result. For example, the optical flow velocity matrix flow_u can be determined according to formula (36):

[0163] The optical flow velocity matrix flow_u can include the optical flow velocity of each pixel in the current frame image.

[0164] In some embodiments, as shown in FIG4, the processing device 120 may also use statistical information generated by the image signal processor of the imaging device to obtain the optical flow velocity of each of the multiple pixels in the current frame image.

[0165] In 402, the processing device 120 (e.g., acquisition module 210) can acquire statistical information generated by the image signal processor of the imaging device.

[0166] Exemplary statistics may include statistics on automatic exposure (AE), automatic white balance (AWB), automatic focus (AF), or any combination thereof. These statistics are automatically generated by the image signal processor.

[0167] For example, the processing device 120 can directly use the automatic exposure (AE) statistics to obtain statistical data (i.e., R) of the previous and current frame images. 统计 G 统计 B 统计 Y 统计 (Four components).

[0168] In 404, the processing device 120 (e.g., acquisition module 210) can acquire the optical flow velocity of each pixel in a plurality of pixels of the current frame image based on statistical information.

[0169] The method of obtaining the optical flow velocity of each pixel in the current frame image based on statistical information is similar to the method of obtaining the optical flow velocity of each pixel in the current frame image based on the original image data of two frames. For example, the processing device 120 can obtain the optical flow velocity of each pixel in the current frame image based on formulas (21), (26)-(28) and (31)-(35).

[0170] As an example only, the processing device 120 can determine the multiples X1 and X2 of the exposure values ​​of the previous frame image and the current frame image relative to the preset exposure value according to formula (21). The processing device 120 can perform exposure normalization processing on the current frame image and the previous frame image to generate second statistical information under the equivalent exposure level, and determine the optical flow velocity of each pixel in the current frame image based on the second statistical information. For example, the processing device 120 can divide the Y component in the statistical information of the original image data of the two frames by the corresponding multiples X1 and X2 respectively to obtain the second statistical information under the equivalent exposure level. The second statistical information of the previous frame image includes R 统计1 G 统计1 B 统计1 Y 统计1 The second statistical information of the current frame image includes R 统计2 G 统计2 B 统计2 Y 统计2 .

[0171] Processing device 120 can process statistical information Y 统计1 and Y 统计2 Mean filtering is performed to reduce noise, resulting in filtered statistical information Y1 and Y2. The processing device 120 can use a preset gradient function to extract the x and y gradients of the filtered statistical information Y1 and Y2 respectively, to obtain the x-direction gradient fx1 and fx2, and the y-direction gradient fy1 and fy2, and determine the averaged x-direction gradient fx1 and y-direction gradient fy1 according to formulas (26) and (27).

[0172] The processing device 120 can determine the frame difference ft by subtracting the filtered statistical information Y1 and Y2 according to formula (28).

[0173] Processing device 120 can measure the optical flow velocity in the x and y directions (flow_x). 初始 and flow_y 初始 The matrix is ​​initialized to all zeros. Optical flow velocity flow_x 初始and flow_y 初始 The matrix includes the initial value of the optical flow velocity for each pixel in the current frame image.

[0174] Processing device 120 can iteratively update the optical flow velocity flow_x in the x and y directions. 初始 and flow_y 初始 For example, processing device 120 can determine whether the current iteration meets the iteration termination condition. The iteration termination condition may include whether the previous iteration number i exceeds an iteration number threshold Tn (an empirical constant) and / or whether the optical flow velocity difference delta between adjacent iterations is less than a preset threshold Tm (an empirical constant that may vary with the resolution of the current pyramid). If the current iteration meets the iteration termination condition, processing device 120 can exit the iteration and determine the optical flow velocity of each pixel of the multiple pixels in the current frame image.

[0175] If the current iteration does not meet the iteration termination condition, processing device 120 can determine whether the current iteration is the first iteration. If the current iteration is the first iteration, processing device 120 can maintain the optical flow velocity flow_x. 初始 and flow_y 初始 The matrix remains unchanged.

[0176] If the current iteration is not the first iteration, the processing device 120 can assign the flow_x and flow_y matrices from the previous iteration to flow_x. 初始 and flow_y 初始 Matrix. Processing device 120 can process the optical flow velocity flow_x in the x and y directions. 初始 and flow_y 初始 The matrix is ​​subjected to mean filtering to obtain the filtered flow_x. 初始 and flow_y 初始 Matrix (i.e., flow_x) 滤波 and flow_y 滤波 (Matrix). Processing device 120 can process the filtered flow_x using local averaging and optical flow constraint methods. 初始 and flow_y 初始 The matrices are used to obtain the flow_x and flow_y matrices of the optical flow velocities in the x and y directions. For example, the flow_x and flow_y matrices of the optical flow velocities in the x and y directions can be determined according to formulas (31)-(34).

[0177] Processing device 120 can determine the optical flow velocity flow_x and flow_y matrices determined in the current iteration and the optical flow velocity flow_x determined in the previous iteration according to formula (35). 初始 and flow_y 初始The difference in optical flow velocity between matrices, delta.

[0178] After determining the optical flow velocity difference delta, the processing device 120 can increment the iteration number i by 1 and determine whether the next iteration is needed.

[0179] After the iteration is completed, the processing device 120 can determine the undirected optical flow velocity flow_u matrix based on the optical flow velocity flow_x and flow_y matrices in the x and y directions according to formula (36).

[0180] According to some embodiments of this specification, the optical flow velocity of each pixel in multiple pixels of the current frame image can be determined directly using statistical information generated by the image signal processor (the statistical information generated by the image signal processor is equivalent to information that has already undergone downsampling processing), without the need to perform multi-layer pyramid calculations to achieve downsampling. This can significantly reduce the amount of computation and improve the efficiency of obtaining optical flow velocity and subsequently determining target exposure parameters.

[0181] Figure 6 is a flowchart of an exemplary process 600 for switching the operating mode of an imaging device according to some embodiments of this specification. In some embodiments, process 600 may be executed by image acquisition system 100. For example, process 600 may be stored in a storage device (e.g., storage device 130) in the form of an instruction set (e.g., an application program). In some embodiments, processing device 120 (e.g., one or more modules shown in Figure 2) may execute the instruction set and accordingly instruct one or more components of image acquisition system 100 to execute process 600. In some embodiments, the operating mode of the imaging device described by operation 306 in Figure 3 may be switched according to process 600.

[0182] An imaging device's operating modes can include multiple brightness modes (i.e., mode one and mode two) and multiple motion modes (e.g., high-speed mode, medium-speed mode, and low-speed mode). A brightness mode refers to the operating mode used by the imaging device in environments with varying brightness. A motion mode refers to the operating mode used by the imaging device when photographing moving objects at different speeds. High-speed mode refers to the operating mode used by the imaging device when photographing high-speed moving objects, medium-speed mode refers to the operating mode used when photographing medium-speed moving objects, and low-speed mode refers to the operating mode used by the imaging device when photographing low-speed moving objects or stationary objects. For example, the shutter speed of an imaging device in high-speed mode is higher than that in medium-speed mode, and the shutter speed in medium-speed mode is higher than that in low-speed mode. This allows the imaging device to minimize motion blur when photographing high-speed moving objects.

[0183] It should be noted that motion modes (such as high-speed mode, medium-speed mode, and low-speed mode) and brightness modes (i.e., the first mode and the second mode) do not conflict and can be set simultaneously in the same imaging device.

[0184] In 602, the processing device 120 (e.g., the determination module 220) can acquire the current motion mode of the imaging device.

[0185] For example, the processing device 120 can obtain the current motion mode of the imaging device from the imaging device or the storage device that stores the working mode.

[0186] In 604, the processing device 120 (e.g., the determination module 220) can determine the target motion mode of the imaging device from multiple motion modes using a mode switching algorithm based on the filtered average optical flow velocity and the current motion mode of the imaging device, and switch the current motion mode of the imaging device to the target motion mode.

[0187] Motion mode refers to a predefined image signal processor configuration mode that adapts to different motion speeds (such as low, medium, and high speeds). Motion modes can include low-speed, medium-speed, and high-speed modes, etc. A motion mode can include a set of image signal processor parameters, covering parameters from multiple modules such as noise reduction, dynamic range, sharpening, and color, to ensure that the imaging device obtains the best overall image quality in the corresponding motion mode.

[0188] A mode switching algorithm is a preset algorithm used to switch between multiple motion modes. For example, processing device 120 can use a mode switching algorithm to determine the target motion mode of the imaging device from multiple motion modes.

[0189] For example only, see Figure 7, which is a schematic diagram of an exemplary process 700 for switching the operating mode of an imaging device according to some embodiments of this specification.

[0190] As shown in Figure 7, in step 702, the processing device 120 can determine whether the current motion mode of the imaging device is a low-speed mode. If the current motion mode is a low-speed mode, the processing device 120 can execute step 704; if the current motion mode is not a low-speed mode, the processing device 120 can execute step 708.

[0191] Step 704, the processing device 120 can determine the filtering speed (i.e., the average optical flow velocity u after filtering). 滤波The system checks if the filtering speed is greater than the mode switching threshold L2. If the filtering speed is greater than the mode switching threshold L2, the imaging device can acquire images in low-speed mode. That is, the continuous frame count M2 in low-speed mode is incremented by one, and step 706 is executed. If the filtering speed is less than or equal to the mode switching threshold L2, the continuous frame counts M1 and M2 are cleared, and the processing device 120 ends process 700. The continuous frame counts M1 and M2 are two counters used to record the duration of continuous operation of the imaging device in the current working mode. The continuous frame count M1 refers to the number of image frames continuously acquired when the filtering speed is less than the corresponding mode switching threshold in the current motion mode, and the continuous frame count M2 refers to the number of image frames continuously acquired when the filtering speed is greater than the corresponding mode switching threshold in the current motion mode. After each motion mode switch or when the filtering speed is not less than the corresponding mode switching threshold, M1 and M2 are cleared to prepare for the next count.

[0192] In step 706, the processing device 120 determines whether the number of continuous frames M2 in low-speed mode is greater than a preset empirical constant K. If the number of continuous frames M2 in low-speed mode is greater than the empirical constant K, the processing device 120 sets the operating mode of the imaging device to medium-speed mode, switches the image signal processor configuration of the imaging device to the corresponding medium-speed configuration, and then resets the number of continuous frames M1 and M2 to zero. The processing device 120 ends process 700. If the number of continuous frames M2 in low-speed mode is less than or equal to the empirical constant K, the processing device 120 ends process 700.

[0193] In step 708, the processing device 120 can determine whether the current mode is a medium-speed mode. If the current motion mode is a medium-speed mode, the processing device 120 can execute step 710; if the current motion mode is not a medium-speed mode, the processing device 120 can execute step 718.

[0194] In step 710, the processing device 120 can determine whether the filtering speed is greater than the mode switching threshold L4. If the filtering speed is greater than the mode switching threshold L4, the imaging device can acquire images in medium-speed mode, that is, the continuous frame count M1 in medium-speed mode is cleared, the continuous frame count M2 is incremented by one, and step 712 is executed. If the filtering speed is less than or equal to the mode switching threshold L4, the processing device 120 can execute step 714.

[0195] In step 712, the processing device 120 determines whether the continuous frame count M2 is greater than a preset empirical constant K. If the continuous frame count M2 is greater than the empirical constant K, the processing device 120 sets the operating mode of the imaging device to high-speed mode, switches the image signal processor configuration of the imaging device to the corresponding high-speed configuration, and then resets the continuous frame counts M1 and M2 to zero. The processing device 120 ends process 700. If the continuous frame count M2 is less than or equal to the empirical constant K, the processing device 120 ends process 700.

[0196] In step 714, the processing device 120 determines whether the filtering speed is less than the mode switching threshold L1. If the filtering speed is less than the mode switching threshold L1, the imaging device can acquire images in low-speed mode; that is, the continuous frame count M1 in low-speed mode is incremented by one, the continuous frame count M2 is cleared, and step 716 is executed. If the filtering speed is greater than or equal to the mode switching threshold L1, the processing device 120 can clear the continuous frame counts M1 and M2 to zero. The processing device 120 can then terminate process 700.

[0197] In step 716, the processing device 120 determines whether the number of continuous frames M1 is greater than a preset empirical constant K. If the number of continuous frames M1 is greater than the empirical constant K, the processing device 120 sets the operating mode of the imaging device to low-speed mode, switches the image signal processor configuration of the imaging device to the corresponding low-speed configuration, and then resets the number of continuous frames M1 and M2 to zero. The processing device 120 ends process 700. If the number of continuous frames M1 is less than or equal to the empirical constant K, the processing device 120 ends process 700.

[0198] In step 718, the processing device 120 determines whether the filtering speed is less than the mode switching threshold L3. If the filtering speed is less than the mode switching threshold L3, the imaging device can acquire images in medium-speed mode, that is, the continuous frame count M1 of the imaging device in medium-speed mode is incremented by one, and step 720 is executed. If the filtering speed is greater than or equal to the mode switching threshold L3, the processing device 120 can reset the continuous frame counts M1 and M2 to zero. The processing device 120 can then terminate process 700.

[0199] In step 720, the processing device 120 determines whether the continuous frame count M1 is greater than a preset empirical constant K. If the continuous frame count M1 is greater than the empirical constant K, the processing device 120 sets the operating mode of the imaging device to medium speed mode, switches the image signal processor configuration of the imaging device to the corresponding medium speed configuration, and then resets the continuous frame counts M1 and M2 to zero. The processing device 120 ends process 700. If the continuous frame count M1 is less than or equal to the empirical constant K, the processing device 120 ends process 700.

[0200] In some embodiments, the relationship between the mode switching thresholds L1-L4 can be L4≥L3≥L2≥L1. As an example only, the relationship between the mode switching thresholds L1-L4 can be shown in Figure 8.

[0201] By introducing a mode-switching algorithm with state memory-based decision logic, the filtered average optical flow velocity can be compared with multiple mode-switching thresholds (L1, L2, L3, L4) to switch the motion mode of the imaging device. Furthermore, by introducing continuous frame counts (M1, M2) and an empirical constant K, the stability of mode switching is increased, preventing frequent jumps near critical values.

[0202] In some embodiments, the mode switching algorithm can be determined based on the filtered average optical flow velocity and the mode switching threshold. For example, the mode switching algorithm can be determined based on the difference between the filtered average optical flow velocity and the mode switching threshold. As an example only, the empirical constant K can be determined according to formula (37): Where, round can be the round-up operator, α can be the sensitivity, L can be any one of the mode switching thresholds L1-L4, and u 滤波 -L can indicate the intensity of the exercise.

[0203] For ease of explanation, we will use the mode switching threshold L2 as an example. The mode switching threshold L2 is used to determine whether to switch from low-speed mode to medium-speed mode. Assume threshold L2 is 50, K is 15, and α is 0.5. When u 滤波 The value is 51 (just exceeding the threshold). Using formula (37), the motion intensity can be determined to be 1, and the empirical constant K is 10. That is, 10 frames need to be waited for. When u 滤波 The value is 80 (far exceeding the threshold). The intensity of the movement can be determined to be 30 using formula (37), and the empirical constant K is 1. That is, the system responds almost immediately (i.e., switches modes immediately), and the system is highly sensitive.

[0204] In some embodiments, the processing device 120 can acquire preset parameter values ​​of the image signal processor corresponding to the target motion mode, and control the image signal processor to operate based on the preset parameter values. For example, when the target motion mode of the imaging device is determined to be a low-speed mode, the processing device 120 can acquire preset parameter values ​​of the image signal processor corresponding to the low-speed mode, and control the image signal processor to operate based on the preset parameter values.

[0205] According to some embodiments of this specification, the entire pre-optimized image signal processor configuration can be switched according to the degree of motion, thereby providing a corresponding operating mode for each motion mode. For example, the high-speed mode configuration can prioritize sharpness, while the low-speed mode can focus on richer colors and dynamic range. This achieves comprehensive and coordinated management of image quality, ensuring that the imaging device outputs the best overall image quality in any motion mode. Furthermore, by adding an empirical constant, the stability of mode switching is improved, preventing frequent jumps near critical values ​​and avoiding unnecessary resource waste. This achieves a dynamic balance between response speed and stability; that is, rapid response during drastic changes and stability at critical states.

[0206] Please refer to Figure 9, which is a schematic diagram of the structure of an exemplary electronic device 900 according to some embodiments of this specification. The electronic device 900 includes a memory 902 and a processor 904. The processor 904 is used to execute program instructions stored in the memory 902 to implement embodiments of the method for determining exposure parameters of an imaging device as described above. In some embodiments, the electronic device 900 may include a microcomputer, a server, a laptop computer, a tablet computer, or any combination thereof.

[0207] Processor 904 controls itself and memory 902 to implement the embodiment of the method for determining exposure parameters of the imaging device described above. Processor 904 can also be referred to as a CPU (Central Processing Unit). Processor 904 may be an integrated circuit chip with signal processing capabilities. Processor 904 can also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. A general-purpose processor can be a microprocessor or any conventional processor. Furthermore, processor 904 can be implemented using integrated circuit chips.

[0208] Please refer to Figure 10, which is a schematic diagram of the structure of an exemplary computer-readable storage medium 1000 according to some embodiments of this specification. The computer-readable storage medium 1000 stores program instructions 1010 executable by a processor, the program instructions 1010 being used to implement embodiments of the method described above for determining exposure parameters of an imaging device.

[0209] It should be further noted that the entity executing the method for determining the exposure parameters of the imaging device can be a device for determining the exposure parameters of the imaging device. For example, the method for determining the exposure parameters of the imaging device can be executed by a terminal device, a server, or other processing device. The terminal device can be a user equipment (UE), computer, mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some embodiments, the method for determining the exposure parameters of the imaging device can be implemented by a processor calling computer-readable instructions stored in memory.

[0210] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0211] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "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. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0212] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0213] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0214] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0215] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0216] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method of determining exposure parameters of an imaging device, characterized by, include: The optical flow velocity of each pixel in a current frame image is obtained, wherein the current frame image is acquired by the imaging device; Based on the optical flow velocity of each pixel, the current brightness and current shutter speed limit of the imaging device are determined; as well as Based on the current brightness and the current shutter speed limit, the target exposure parameters of the imaging device are determined.

2. The method of claim 1, wherein, The process of obtaining the optical flow velocity of each pixel in the multiple pixels of the current frame image includes: Obtain statistical information generated by the image signal processor of the imaging device; and Based on the statistical information, the optical flow velocity of each pixel in the current frame image is obtained.

3. The method of claim 1, wherein, The current brightness is determined based on the following method: Based on the optical flow velocity of each pixel, the current frame image is segmented into a high-speed motion region and a low-speed motion region, wherein the high-speed motion region includes a first pixel among the plurality of pixels, and the low-speed motion region includes a second pixel among the plurality of pixels. A first brightness weight and a second brightness weight are assigned to each first pixel and each second pixel, respectively, wherein the first brightness weight is less than the second brightness weight; The current brightness is determined based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image.

4. The method of claim 3, wherein, Determining the current brightness based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image includes: The initial current brightness is determined based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image. The brightness filtering weights of the current frame image are determined based on preset filtering parameters. Based on the brightness filtering weights and the initial current brightness, the current frame image is filtered to determine the current brightness.

5. The method of claim 3, wherein, The current shutter speed limit is determined based on the following method: The average optical flow velocity of the high-speed motion region is determined based on the optical flow velocity of each first pixel in the high-speed motion region. as well as The current shutter speed limit is determined based on the average optical flow velocity, wherein the current shutter speed limit is inversely proportional to the average optical flow velocity.

6. The method of claim 4, wherein, The method further includes: The average optical flow velocity is filtered to obtain the filtered average optical flow velocity.

7. The method of claim 6, wherein, The method further includes: Based on the filtered average optical flow velocity, the noise reduction parameters in the image signal processor of the imaging device are adjusted. The noise reduction parameters include at least one of temporal intensity, spatial intensity, temporal filtering coefficient, edge preservation threshold, or brightness dependence.

8. The method of claim 6, wherein, The method further includes: Obtain the current motion mode of the imaging device; and Based on the filtered average optical flow velocity and the current motion mode of the imaging device, a mode switching algorithm is used to determine the target motion mode of the imaging device from multiple motion modes, and the current motion mode of the imaging device is switched to the target motion mode.

9. The method of claim 8, wherein, The mode switching algorithm is determined based on the filtered average optical flow velocity and the mode switching threshold.

10. The method according to any one of claims 5 to 9, characterized in that, The method further includes: In response to the imaging device being in night mode and the presence of a target light source in the environment in which the imaging device is located, the current shutter speed limit is adjusted based on the current shutter speed limit and the adjustment coefficient corresponding to the target light source.

11. The method of claim 10, wherein, The method further includes: The imaging device is determined to be in the night mode based on a preset determination algorithm, wherein at least one determination threshold in the preset determination algorithm is determined based on the environment in which the imaging device is located.

12. The method of claim 1, wherein, The process of determining the target exposure parameters of the imaging device based on the current brightness and the current shutter speed limit includes: Based on the current shutter speed limit, the exposure range is segmented to determine multiple exposure segments; Determine the equivalent multiple of the target exposure gain based on the target brightness and the current brightness; The target exposure segment is determined from the plurality of exposure segments based on the target exposure gain equivalent multiple; and The target exposure parameters are determined based on the target exposure segments.

13. The method of claim 12, wherein, Determining the equivalent multiple of the target exposure gain based on the target brightness and the current brightness includes: Based on the target brightness and the current brightness, determine the equivalent gain factor to be adjusted; Obtain the current shutter speed and current exposure gain corresponding to the current frame image; Determine the current gain equivalent factor based on the current shutter speed and the current exposure gain; and The target gain equivalent multiple is determined by summing the current gain equivalent multiple and the required gain equivalent multiple.

14. The method of claim 1, wherein, The process of obtaining the optical flow velocity of each pixel in the multiple pixels of the current frame image includes: Exposure normalization is performed on the current frame image and the previous frame image to generate second statistical information under equivalent exposure levels; and Based on the second statistical information, the optical flow velocity of each pixel in the current frame image is determined.

15. A system for determining exposure parameters of an imaging device, comprising: Storage devices that store computer instructions; A processor, connected to the storage device, causes the system to perform the following operations when executing the computer instructions: The optical flow velocity of each pixel in a current frame image is obtained, wherein the current frame image is acquired by the imaging device; Based on the optical flow velocity of each pixel, the current brightness and current shutter speed limit of the imaging device are determined; as well as Based on the current brightness and the current shutter speed limit, the target exposure parameters of the imaging device are determined.

16. The system of claim 15, wherein, The process of obtaining the optical flow velocity of each pixel in the multiple pixels of the current frame image includes: Obtain statistical information generated by the image signal processor of the imaging device; and Based on the statistical information, the optical flow velocity of each pixel in the current frame image is obtained.

17. The system of claim 15, wherein, The current brightness is determined based on the following method: Based on the optical flow velocity of each pixel, the current frame image is segmented into a high-speed motion region and a low-speed motion region, wherein the high-speed motion region includes a first pixel among the plurality of pixels, and the low-speed motion region includes a second pixel among the plurality of pixels. A first brightness weight and a second brightness weight are assigned to each first pixel and each second pixel, respectively, wherein the first brightness weight is less than the second brightness weight; The current brightness is determined based on the first brightness weight of the first pixel, the second brightness weight of the second pixel, and the initial brightness information of the current frame image.

18. The system of claim 15, wherein, The process of determining the target exposure parameters of the imaging device based on the current brightness and the current shutter speed limit includes: Based on the current shutter speed limit, the exposure range is segmented to determine multiple exposure segments; Determine the equivalent multiple of the target exposure gain based on the target brightness and the current brightness; The target exposure segment is determined from the plurality of exposure segments based on the target exposure gain equivalent multiple; and The target exposure parameters are determined based on the target exposure segments.

19. The system of claim 15, wherein, The process of obtaining the optical flow velocity of each pixel in the multiple pixels of the current frame image includes: Exposure normalization is performed on the current frame image and the previous frame image to generate second statistical information under equivalent exposure levels; and Based on the second statistical information, the optical flow velocity of each pixel in the current frame image is determined.

20. A non-transitory computer-readable medium comprising executable instructions, which, when executed by at least one processor, perform a method for determining exposure parameters of an imaging apparatus, the method comprising: The optical flow velocity of each pixel in a current frame image is obtained, wherein the current frame image is acquired by the imaging device; Based on the optical flow velocity of each pixel, the current brightness and current shutter speed limit of the imaging device are determined; as well as Based on the current brightness and the current shutter speed limit, the target exposure parameters of the imaging device are determined.

Images