WO2020094091A1 - Procédé de capture d'image, caméra de surveillance et système de surveillance - Google Patents

Procédé de capture d'image, caméra de surveillance et système de surveillance Download PDF

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Publication number
WO2020094091A1
WO2020094091A1 PCT/CN2019/116219 CN2019116219W WO2020094091A1 WO 2020094091 A1 WO2020094091 A1 WO 2020094091A1 CN 2019116219 W CN2019116219 W CN 2019116219W WO 2020094091 A1 WO2020094091 A1 WO 2020094091A1
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WO
WIPO (PCT)
Prior art keywords
face target
image
target
face
video frame
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Ceased
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PCT/CN2019/116219
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English (en)
Chinese (zh)
Inventor
王晶晶
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Hikvision Digital Technology Co Ltd
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Hangzhou Hikvision Digital Technology Co Ltd
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Publication of WO2020094091A1 publication Critical patent/WO2020094091A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/61—Control of cameras or camera modules based on recognised objects
    • H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules

Definitions

  • the present application relates to the technical field of video surveillance, in particular to an image capture method, surveillance camera and surveillance system.
  • the surveillance camera captures the face object appearing in the scene, and uploads the captured image to the comparison system.
  • the comparison system extracts the face features from the captured image, and extracts the extracted face features and the black list face features Perform a comparison, and if the similarity of the comparison is greater than a certain threshold, an alarm is generated.
  • the surveillance camera Whenever the surveillance camera detects a face target, it will capture the face target. However, the face target generally appears continuously in the video, and the surveillance camera will capture the captured image in multiple consecutive video frames. All are uploaded to the comparison system, which brings huge pressure to the transmission and storage of the captured images and the data processing of the comparison system.
  • the comparison system can receive the captured image at a fixed frequency , Greatly reducing the number of captured images uploaded by the surveillance camera to the comparison system.
  • the face target may be poorly recognizable due to blurring, occlusion, and other reasons. If the recognizable face target in each captured image received by the system is If it is poor, it will affect the similarity of the comparison, causing false alarms or false negatives, resulting in lower accuracy of the comparison results.
  • the purpose of the embodiments of the present application is to provide an image capture method, a monitoring camera, and a monitoring system, so as to ensure that the comparison result of the comparison system has high accuracy.
  • the specific technical solutions are as follows:
  • an image capture method which includes:
  • the face target image with the best image quality in the preset snapshot interval is uploaded to the comparison system as the captured image of the specified face target.
  • the face target image with the best image quality in the preset capture interval is uploaded to the comparison system as the captured image of the specified face target
  • the method also includes:
  • the same face target in the current video frame and the previous video frame is determined.
  • the method of determining the image quality includes:
  • the image quality of the face target image is determined.
  • uploading the face target image with the best image quality within the preset snapshot interval as the captured image of the specified face target to the comparison system includes:
  • the step of uploading the face target image with the best image quality in the preset capture interval as the captured image of the specified face target to the comparison system includes:
  • the currently cached face target image is uploaded to the comparison system as a captured image of the specified face target.
  • the step of uploading the face target image with the best image quality in the preset capture interval as the captured image of the specified face target to the comparison system includes:
  • an embodiment of the present application provides a surveillance camera, including a surveillance camera, a processor, and a memory, where,
  • Surveillance camera used to collect the current video frame
  • Memory used to store computer programs
  • the processor when used to execute the computer program stored on the memory, implements the following steps:
  • the face target image with the best image quality in the preset snapshot interval is uploaded to the comparison system as the captured image of the specified face target.
  • the same face target in the current video frame and the previous video frame is determined.
  • the image quality of the face target image is determined.
  • the processor implements the step of uploading the face target image with the best image quality in the preset capture interval as the captured image of the specified face target to the comparison system. Implement the following steps:
  • the processor implements the step of uploading the face target image with the best image quality within the preset capture interval as the captured image of the specified face target to the comparison system
  • the specific steps are as follows:
  • the currently cached face target image is uploaded to the comparison system as a captured image of the specified face target.
  • the processor implements the step of uploading the face target image with the best image quality within the preset capture interval as the captured image of the specified face target to the comparison system
  • the specific steps are as follows:
  • an embodiment of the present application provides a machine-readable storage medium in which a computer program is stored.
  • the computer program is executed by a processor, the image capture method provided in the first aspect of the embodiment of the present application is implemented .
  • an embodiment of the present application provides an application program for execution at runtime: the image capturing method provided in the first aspect of the embodiment of the present application.
  • an embodiment of the present application provides a monitoring system, including a monitoring camera and a comparison system;
  • Surveillance camera used to collect the current video frame; perform face target detection on the current video frame to determine the face target image of each face target in the current video frame; for the specified face target, the preset capture interval is met in the current video frame , The face target image with the best image quality within the preset capture interval is uploaded to the comparison system as the captured image of the specified face target;
  • the comparison system is used to compare and alarm the captured images.
  • the monitoring camera collects the current video frame, performs face target detection on the current video frame, and determines the face target image of each face target in the current video frame For the specified face target, when the current video frame meets the preset capture interval, the face target image with the best image quality in the preset capture interval is uploaded to the comparison system as the captured image of the specified face target.
  • FIG. 1 is a schematic flowchart of an image capture method according to an embodiment of this application
  • FIG. 2 is a schematic diagram of a snapshot effect of an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a snapshot effect of another embodiment of the present application.
  • FIG. 4 is a schematic diagram of a snapshot effect according to another embodiment of the application.
  • FIG. 5 is a schematic diagram of a snapshot effect of yet another embodiment of the present application.
  • FIG. 6 is a schematic diagram of a process of capturing and compressing a face target image according to an embodiment of the present application
  • FIG. 7 is a schematic structural diagram of a surveillance camera according to an embodiment of this application.
  • FIG. 8 is a schematic structural diagram of a monitoring system according to an embodiment of the present application.
  • the monitoring system mainly captures, compares and alarms face targets.
  • the monitoring system includes a surveillance camera and a comparison system.
  • the comparison system can be a background server, which is mainly used to realize feature extraction, face comparison and alarm functions.
  • the embodiments of the present application provide an image capture method, a monitoring camera, a machine-readable storage medium, and a monitoring system.
  • the execution subject of the image capture method provided in the embodiments of the present application may be a surveillance camera (for example, a smart camera, a network camera, etc.) in the surveillance system, and the surveillance camera may include at least a surveillance camera and a processor equipped with a core processing chip .
  • the method for implementing the image capturing method provided by the embodiments of the present application may be at least one method of software, hardware circuits, and logic circuits provided in the monitoring camera.
  • an image capture method provided by an embodiment of the present application may include the following steps:
  • S101 Collect the current video frame.
  • Surveillance cameras can be installed in all corners of the city, for example, community entrances, intersections, parks, stadiums, etc. Here, there is no specific requirement for the specific location, angle and resolution of the surveillance camera, which can meet the coverage. As long as possible, the requirements for capturing the face target clearly.
  • the surveillance camera can shoot the surveillance scene in real time to obtain video data of the surveillance scene.
  • the surveillance camera can shoot the surveillance scene in real time to obtain the video data of the surveillance scene, and the video data includes the video frames of each frame and the time stamp of each video frame collected.
  • the current video frame collected needs to be processed.
  • S102 Perform face target detection on the current video frame to determine the face target image of each face target in the current video frame.
  • the preset target detection algorithm can be a traditional feature matching algorithm, which can determine the current video frame by face features such as eyes, nose, mouth, ears, etc. Whether the target in is a face target, if it is a face target, a certain area around the face target is divided into a face target frame, and the image in the face target frame or the image within a certain range of the face target frame is a person Face target image; the preset target detection algorithm can also be a more popular intelligent detection algorithm, such as deep neural network.
  • the network model of deep neural network can be obtained by training a large number of face images, by inputting the current video frame into the depth
  • the neural network can obtain the interest area of the face target in the current video frame, and the image in the interest area of the face target or the image within a certain range of the interest area of the face target is the face target image.
  • other methods that can detect the face target in the video frame also belong to the protection scope of the embodiments of the present application, and details are not repeated here.
  • S103 For the specified face target, when the current video frame meets the preset capture interval, upload the face target image with the best image quality in the preset capture interval as the captured image of the specified face target to the comparison system.
  • the specified face target can be any face target previously detected.
  • the first video frame in which the specified face target is detected can be regarded as the start frame, and every subsequent frame is collected , The frame number is superimposed once. If the number of frames in the current video frame satisfies the preset snapshot interval, it means that the condition for grouping to determine the captured image is reached.
  • the face target with the best image quality within this preset snapshot interval needs to be The image is taken as the captured image of the specified face target.
  • the face target A For example, for the face target A, the face target A is detected for the first time in the fifth frame, then the fifth frame is recorded as the start frame of the face target A, and the current video frame is the 20th frame, that is, the person is detected In the fifteenth frame after face target A, if the preset capture interval is 15 frames, the face target image with the best image quality in these 15 frames can be used as the capture image of face target A.
  • the current video frame meets the preset snapshot interval, it is equivalent to grouping the video frame sequence that detects the specified face target.
  • the video frames in a snapshot interval are divided into a group, and the preset snapshot interval is the number of video frames. For example, if the snapshot interval is set to 10 frames, as shown in FIG. 2, the shaded part indicates that the image quality of the face target image in the video frame is the best.
  • the captured image includes: the corresponding face target specified in frames 1-10
  • the face target image with the highest image quality (the face target image corresponding to the face target in frame 6), and the face image with the highest image quality corresponding to the face target in frames 11-20 (frame 19
  • the face target image corresponding to the face target in frame) the face target image with the highest image quality corresponding to the face target in frames 21-30 (the face target image corresponding to the face target in frame 23),
  • the face image with the highest image quality corresponding to the face target in frames 31-40 (the face target image corresponding to the face target in frame 40), and the face image corresponding to the face target in frames 41-50
  • the face image with the highest image quality (Face target image corresponding to the face target in frame 47).
  • the preset snapshot interval may be fixed or variable. For example, in the first three periods, the preset snapshot interval may be set to 15 frames, and in the latter period, the preset snapshot interval may be set to 25 frames, and so on.
  • the image quality analysis of the face target images of each face target in the video is required to determine the face target image with the best image quality.
  • determine the image quality The specific way can be:
  • the image quality of the face target image there are many factors that affect the image quality of the face target image, such as the degree to which the face target is blocked in the face target image, the imaging clarity of the face target in the face target image, and the pose of the face target in the face target image and many more.
  • the image quality of the face target image such as contrast, brightness, etc., which are not listed here.
  • the preset quality analysis algorithm can also assign, for example, good, good, medium, and poor evaluation results to the face target image.
  • the face target quality parameter refers to the parameter that affects the image quality of the face target image, mainly including the face target's posture, degree of occlusion, and imaging clarity.
  • the facial target quality parameters such as the posture information, degree of occlusion and sharpness of the facial target are based on the comprehensive consideration of the effects of different facial target quality parameters on the image quality to obtain the image quality of the facial target image. For example, in the current video frame, in the face target image in the face target frame, the face target A is completely frontal, the face is occluded by 1/10, and the definition is very high, the image quality of the face target image can be determined For superiority, or, to quantify the image quality, assign an image quality score of 9.
  • the way to determine the face target image with the best image quality may be to cache the determined face target image and image quality, and after collecting the current video frame, compare the face target image in the current video frame with the cached The image quality of the face target image. If the image quality of the face target image in the current video frame is better, the face target image in the current video frame is cached, overwriting the original face target image cached.
  • the face target image with the best image quality can be directly determined as the captured image; of course, the way to determine the face target image with the best image quality can also be every time a face is detected
  • the target image caches the face target image and image quality until it stops when the current video frame meets the preset capture interval, and then compares the whole image to find the face target image with the best image quality as the captured image. There is no specific limit here.
  • the snapshot camera can select a preset number of snapshot images from the snapshot images determined in each preset snapshot interval to upload, instead of uploading all the determined snapshot images All upload, the preset number can be one or more.
  • the preset number is usually set to multiple. For the selection of multiple captured images, it can be multiple captured images with the highest image quality. It may be a plurality of captured images determined first, and no specific limitation is made here.
  • the image capturing method provided by the embodiment of the present application may further perform the following steps:
  • the same face target in the current video and the previous video frame is determined.
  • the preset target tracking algorithm can be a currently popular intelligent algorithm by targeting the face target in each video frame Frame association generates target trajectories corresponding to different face targets.
  • the same target trajectory corresponds to the same face target. In this way, the same face target in the current video frame and the previous video frame can be determined according to the target trajectory.
  • the target frame can also be realized by matching the target frame after performing face target detection on the current video frame and the previous video frame. If the face target frame can be matched, it means that the two face target frames correspond to the same face target.
  • S103 may specifically be:
  • the preset snapshot interval is 1 frame, it means that the capture of the surveillance camera is actually a full-frame snapshot, that is, for the specified face target, as long as the face target image of the specified face target is detected in the current video frame, it will be The target image of the face is taken as a captured image, and the effect picture of the captured image is shown in FIG. 3.
  • S103 may specifically be:
  • the currently cached face target image is uploaded to the comparison system as a captured image of the specified face target.
  • each preset snapshot interval needs to be captured.
  • the snapshot can be captured in increasing quality to ensure that the captured image uploaded to the comparison system is the best image quality of all captured images.
  • the optimal image quality is cached. If the image quality of the currently cached face target image is better than the previously cached faces Only the image quality of the target image is taken as the captured image of the face image currently cached, otherwise it will not be captured.
  • the face image with the highest image quality corresponding to the specified face target in frames 11-10 is the face target image corresponding to the specified face target in frame 6, then the person in frame 6
  • the face target image is taken as a captured image
  • the face image with the highest image quality corresponding to the specified face target in frames 11-20 is the face target image corresponding to the specified face target in frame 19, but specified in frame 19
  • the image quality of the face target image corresponding to the face target is inferior to the face target image corresponding to the specified face target in frame 6, so no snapshot is taken;
  • the face target image is the face target image corresponding to the face target specified in frame 23, and the image quality of the face target image corresponding to the face target specified in frame 23 is higher than the face target specified in frame 6 and frame 19
  • the corresponding face target image is better, so the face target image in frame 23 is taken as the captured image; the face image with the highest image quality corresponding to the specified face target in frames 31-40 is in frame 40
  • surveillance cameras that only need to upload a face target image with the best image quality as a captured image to the comparison system, so that the image quality of all face target images can be compared and the best image quality can be selected from It is better to upload as a captured image, or to use an overlay cache method.
  • the effect picture of the snapshot is shown in FIG. 5.
  • the image quality of the face target image corresponding to the specified face target in the 40th frame is the best. You only need to determine the face target image in the 40th frame as the captured image. Compare systems.
  • S103 may specifically be:
  • the captured image can be cached in the cache area, and then the entire compression upload is performed.
  • the process is shown in FIG. 6.
  • JPEG Joint Photographic Experts Group
  • the way to determine the end of the capture of the face target may be that the matching degree of the continuous multiple frames to the face target is very low, then the end of the capture of the face target may be determined, or the target tracking algorithm may be used to determine the loss of the face target , That is, a face target cannot be tracked in multiple consecutive frames, and it can be determined that the capture of the face target ends.
  • the monitoring camera collects the current video frame and performs face target detection on the current video frame to determine the face target image of each face target in the current video frame.
  • the current video frame meets the pre-
  • the snapshot interval is set, the face target image with the best image quality in the preset snapshot interval is uploaded to the comparison system as the captured image of the specified face target.
  • face target detection is performed on the current video frame, and when the current video frame meets the preset capture interval, for the specified face target, the face image with the best image quality within the preset capture interval Uploading the captured image of the specified face target to the comparison system is equivalent to grouping the video frame sequence for detecting the specified face target, which can be used to capture the specified face target in different capture intervals respectively.
  • the image quality is optimal within the capture interval, to ensure that the surveillance camera uploads multiple capture images with higher image quality to the comparison system, and these capture images are not concentrated in a certain period of time because they are in different time intervals. It has a high degree of richness, which ensures the recognizability of face targets, and thus ensures that the comparison results of the comparison system have a high accuracy.
  • an embodiment of the present application provides a surveillance camera. As shown in FIG. 7, it includes a surveillance camera 701, a processor 702, and a memory 703, where,
  • Surveillance camera 701 used to collect the current video frame
  • Memory 703 used to store computer programs
  • the face target image with the best image quality in the preset snapshot interval is uploaded to the comparison system as the captured image of the specified face target.
  • processor 702 executes the computer program stored on the memory, the following steps may also be implemented:
  • the same face target in the current video frame and the previous video frame is determined.
  • processor 702 executes the computer program stored on the memory, the following steps may also be implemented:
  • the image quality of the face target image is determined.
  • the processor 702 when implementing the step of uploading the face target image with the best image quality in the preset snapshot interval as the captured image of the specified face target to the comparison system, the specific steps can be achieved as follows:
  • processor 702 implements the step of uploading the face target image with the best image quality within the preset capture interval as the captured image of the specified face target to the comparison system, the following steps may be specifically implemented:
  • the currently cached face target image is uploaded to the comparison system as a captured image of the specified face target.
  • processor 702 implements the step of uploading the face target image with the best image quality within the preset capture interval as the captured image of the specified face target to the comparison system, the following steps may be specifically implemented:
  • the above memory may include RAM (Random Access Memory, random access memory), or may include NVM (Non-Volatile Memory, non-volatile memory), for example, at least one disk memory.
  • the memory may also be at least one storage device located away from the processor.
  • the above processor may be a general-purpose processor, including CPU (Central Processing Unit), NP (Network Processor), etc .; it may also be DSP (Digital Signal Processing, digital signal processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • CPU Central Processing Unit
  • NP Network Processor
  • DSP Digital Signal Processing, digital signal processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • Other programmable logic devices discrete gates or transistor logic devices, discrete hardware components.
  • Data can be transmitted between the surveillance camera 701, the processor 702, and the memory 703 through a wired connection or a wireless connection, and the surveillance camera can communicate with the comparison system through a wired communication interface or a wireless communication interface.
  • 7 is only an example of data transmission between the surveillance camera 701, the processor 702, and the memory 703 through the bus, and is not intended as a limitation of a specific connection method.
  • the processor of the surveillance camera can read the computer program stored in the memory and run the computer program to realize that the surveillance camera collects the current video frame and performs face target detection on the current video frame to determine The face target image of each face target in the current video frame, for the specified face target, when the current video frame meets the preset snapshot interval, the face target image with the best image quality in the preset snapshot interval is used as the designated person
  • the captured image of the face target is uploaded to the comparison system.
  • embodiments of the present application also provide a machine-readable storage medium, and the machine-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the image capturing method provided by the embodiment of the present application All steps.
  • the machine-readable storage medium stores a computer program that executes the image capture method provided by the embodiment of the present application at runtime, so it can be achieved that the surveillance camera performs face targeting on the current video frame by collecting the current video frame Detection to determine the face target image of each face target in the current video frame. For the specified face target, when the current video frame meets the preset snapshot interval, the face target image with the best image quality within the preset snapshot interval is used as The captured image of the designated face target is uploaded to the comparison system.
  • An embodiment of the present application also provides an application program for executing at run time: all steps of the image capturing method provided by the embodiment of the present application.
  • the application program executes the image capturing method provided in the embodiment of the present application when it is running, so that it can realize that the surveillance camera performs face target detection on the current video frame by collecting the current video frame to determine the current video frame.
  • the face target image of each face target, for the specified face target, when the current video frame meets the preset capture interval, the face target image with the best image quality within the preset capture interval is used as the snapshot of the specified face target
  • the image is uploaded to the comparison system.
  • the monitoring system may include a monitoring camera 810 and a comparison system 820;
  • Surveillance camera 810 used to collect the current video frame; perform face target detection on the current video frame to determine the face target image of each face target in the current video frame; for the specified face target, the current video frame meets the preset snapshot At intervals, the face target image with the best image quality within the preset capture interval is uploaded to the comparison system as the captured image of the specified face target;
  • the comparison system 820 is used for comparing and alarming the captured images.
  • the monitoring camera 810 can also be used to implement all the steps provided in the above method embodiments, which will not be repeated here.
  • the comparison system 820 compares and alarms the captured image, which may include: extracting the captured image feature, and comparing the extracted facial features with the facial features in the blacklist, if the similarity of the comparison is greater than a certain Threshold, then alarm.
  • the monitoring camera collects the current video frame and performs face target detection on the current video frame to determine the face target image of each face target in the current video frame.
  • the current video frame meets the pre-
  • the snapshot interval is set, the face target image with the best image quality in the preset snapshot interval is uploaded to the comparison system as the captured image of the specified face target.
  • face target detection is performed on the current video frame, and when the current video frame meets the preset capture interval, for the specified face target, the face image with the best image quality within the preset capture interval Uploading the captured image of the specified face target to the comparison system is equivalent to grouping the video frame sequence for detecting the specified face target, which can be used to capture the specified face target in different capture intervals respectively.
  • the image quality is optimal within the capture interval, to ensure that the surveillance camera uploads multiple capture images with higher image quality to the comparison system, and these capture images are not concentrated in a certain period of time because they are in different time intervals. It has a high degree of richness, which ensures the recognizability of face targets, and thus ensures that the comparison results of the comparison system have a high accuracy.

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Abstract

Les modes de réalisation de la présente invention concernent un procédé de capture d'image, une caméra de surveillance et un système de surveillance. Le procédé de capture d'image consiste à : capturer une trame de vidéo actuelle ; effectuer une détection de visage humain cible sur la trame de vidéo actuelle pour déterminer une image de visage humain cible de chaque visage humain cible dans la trame de vidéo actuelle ; et viser un visage humain cible désigné, considérer l'image du visage humain cible avec une qualité d'image optimale dans un intervalle de capture prédéfini comme l'image capturée du visage humain cible désigné lorsque la trame de vidéo actuelle respecte l'intervalle de capture prédéfini, et téléverser celle-ci vers un système de comparaison. Selon la présente solution, il peut être assuré qu'un résultat de comparaison du système de comparaison présente une plus grande précision.
PCT/CN2019/116219 2018-11-07 2019-11-07 Procédé de capture d'image, caméra de surveillance et système de surveillance Ceased WO2020094091A1 (fr)

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CN111914781A (zh) * 2020-08-10 2020-11-10 杭州海康威视数字技术股份有限公司 一种人脸图像处理的方法及装置
CN112036346A (zh) * 2020-09-04 2020-12-04 贵州东冠科技有限公司 基于人员分类为基础的实有人员管理系统
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CN112036346A (zh) * 2020-09-04 2020-12-04 贵州东冠科技有限公司 基于人员分类为基础的实有人员管理系统
CN112132022A (zh) * 2020-09-22 2020-12-25 平安科技（深圳）有限公司 人脸抓拍架构及其人脸抓拍方法、装置、设备及存储介质
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CN113438417A (zh) * 2021-06-22 2021-09-24 上海云从汇临人工智能科技有限公司 视频抓拍待识别物的方法、系统、介质及装置
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