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Real-time video stream-oriented edge environment behavior recognition system

A real-time video stream and recognition system technology, applied in the field of computer vision research, can solve the problems of occupying large network resources, service delay, privacy leakage, etc., and achieve the effects of improving real-time performance, improving detection efficiency, and saving computing resources

Pending Publication Date: 2021-11-02
GUILIN UNIV OF ELECTRONIC TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Therefore, the current transmission of massive real-time streaming data will occupy a large amount of network resources, which will lead to service delays; when cloud services face private data in sensitive scenarios, there is a risk of privacy leakage

Method used

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  • Real-time video stream-oriented edge environment behavior recognition system
  • Real-time video stream-oriented edge environment behavior recognition system
  • Real-time video stream-oriented edge environment behavior recognition system

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Experimental program
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Embodiment

[0040] An edge environment behavior recognition system for real-time video streams, such as figure 1 As shown, it includes a video stream acquisition module, a video stream storage module, a video stream preprocessing module, a cache module, a sliding window positioning module, a behavior recognition module and a user interface module;

[0041] The video stream acquisition module is used to obtain video stream data in real time, and transmit the acquired data to the video stream storage module and the user interface module, and the user interface module displays the originally obtained video stream data; specifically: through Different types of cameras or smart devices containing cameras, real-time acquisition of raw video data in the monitoring area, and provide real-time streams for the entire system. These original video streams will be transmitted to the user interface and stored in a persistent database, which can provide support for subsequent data parallel processing. I...

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Abstract

The invention discloses a real-time video stream-oriented edge environment behavior recognition system, which comprises a video stream acquisition module, a video stream storage module, a video stream preprocessing module, a cache module, a sliding window positioning module, a behavior recognition module and a user interface module; the sliding window positioning module starts an online detection method based on the action of the elastic jumping sliding window, a large amount of computing resources are saved by positioning the position of the action, and meanwhile, large loss of the detection performance of an original model with excellent performance due to the fact that frame-by-frame sliding of the sliding window is abandoned is avoided; a model lightweight effect is realized to a certain extent, a large amount of invalid data is prevented from being sent to a behavior identification module, the behavior identification efficiency is improved, the identification real-time performance is improved, and the privacy security problem of data acquired by edge equipment in a sensitive scene is better protected through data localization processing; and compared with a behavior identification service generally based on a cloud center, the system is more beneficial to the use of edge devices with limited resources.

Description

technical field [0001] The invention relates to the technical field of computer vision research, in particular to an edge environment behavior recognition system oriented to real-time video streams. Background technique [0002] After rapid development in recent years, video behavior recognition technology has been widely used in many fields such as security, medical treatment, and human-computer interaction. In the past, behavior recognition services were mostly deployed on cloud centers, but as behavior recognition research gradually shifts from offline processing to real-time computing and analysis of video streams, and a large number of video data sources are gradually transferred to edge nodes, cloud computing is becoming Not suitable for behavior recognition services. Because cloud services far away from data sources may face problems such as network congestion and insufficient bandwidth when receiving data, they cannot meet high real-time requirements. [0003] The ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/36G06K9/38G06K9/62H04L29/06
CPCH04L65/75G06F18/2415
Inventor 翟仲毅陈晓峰赵岭忠
Owner GUILIN UNIV OF ELECTRONIC TECH
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