Real-time monitoring and threat analysis method and system based on deep learning
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A technology for threat analysis and real-time monitoring, applied in neural learning methods, transmission systems, closed-circuit television systems, etc. The effect of pressure, reducing labor cost and improving security efficiency
Active Publication Date: 2018-03-16
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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[0004] The purpose of the present invention is: the present invention provides a real-time monitoring threat analysis method and system based on deep learning, which solves the problem of poor real-time performance and accuracy caused by the large amount of monitoring dat
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Embodiment 1
[0039] A real-time monitoring threat analysis method based on deep learning, comprising the following steps:
[0040] Step 1: the video acquisition unit collects video information;
[0041] Step 2: The video analysis and processing unit sequentially performs image preprocessing based on the neural network, target detection based on the grid extraction layer, and threat analysis based on the deep neural network to obtain analysis results and send them to the video cloud processing server;
[0042] Step 2 includes the following steps:
[0043] Step 2.1: The image preprocessing module in the video analysis and processing unit sequentially decodes, decomposes, down-samples and normalizes the video information to obtain several frames of monitoring image data;
[0044] Step 2.2: The target detection module in the video analysis processing unit monitors the image data for each frame based on the neural network using structural layers such as the convolutional layer and the grid ext...
Embodiment 2
[0057] First, the video acquisition unit performs image acquisition on the monitoring area, and the acquisition device adopts a high-definition surveillance camera or a camera on a wearable device or a camera of a mobile phone.
[0058] The image data collected by the video acquisition unit is encoded by the video encoder and then transmitted to the video cloud processing server through a wireless network or an optical fiber cable network. The video cloud processing server stores the obtained data in the video cloud processing server before processing the data. backup, and then send the data to the video analysis and processing unit for processing.
[0059] The video analysis and processing unit preprocesses the surveillance video data.
[0060] The preprocessing steps are:
[0061] S101: Decoding the surveillance video, and then decomposing the video into images frame by frame;
[0062] S102: Downsampling each frame of image to change the image to a resolution of 448*448, s...
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Abstract
The present invention discloses a real-time monitoring and threat analysis method and system based on deep learning and relates to the field of intelligent monitoring based on deep learning. The method comprises the following steps that: 1) a video acquisition unit collects video information; 2) a video analysis and processing unit sequentially performs neural network-based image preprocessing, grid extraction layer-based target detection, and deep neural network-based threat analysis to obtain analysis results and sends the analysis results to a video cloud processing server; and 3) the videocloud processing server transmits the analysis results to a video display unit to output the analysis results, and real-time monitoring and threat analysis are completed. The technical scheme of thepresent invention solves the problems of poor real-time performance and accuracy due to the large amount of monitoring data in the existing monitoring system, and low efficiency due to the large workload of the monitoring staff, and achieves effects of reducing the pressure of analyzing the monitoring data by the security personnel, reducing labor costs and improving security efficiency.
Description
technical field [0001] The invention relates to the field of intelligent monitoring based on deep learning, in particular to a real-time monitoring threat analysis method and system based on deep learning. Background technique [0002] Convolutional neural network is a deep learning model that can automatically extract features and perform sampling. It has high use value in the field of image processing; it has the characteristics of fast running speed, good adaptability, efficient extraction of image features and translation invariance. , suitable for image processing. [0003] In modern society, video surveillance system plays a very important role in the field of security; nowadays, surveillance cameras can be seen everywhere. According to statistics, there are more than 200 million surveillance cameras in the world, which does not include all kinds of cameras that can be converted to surveillance at any time. Devices, such as mobile phones, notebooks, smart glasses, etc...
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