Real-time video monitoring method and system integrating edge computing with cloud computing

Abstract

The invention discloses a real-time video monitoring method integrating edge computing with cloud computing. The real-time video monitoring method comprises the following steps: constructing a sampletraining library, training a CNN convolution neutral network by using image information in the sample training library, constructing a CNN model structure as training chip content, and embedding the training chip in front edge equipment; extracting a monitoring video of the edge equipment in real time, decomposing an original video into multiple frame images; performing feature and similarity computing on multiple frame images through the CNN training chip; storing the extracted similarity picture and information to remote cloud-side equipment to backup and manage for a long time, providing for the supervision department to use, analyze and compute, pushing the message to related department to implement the alarm to the related department. The invention further discloses a real-time videomonitoring system. The method disclosed by the invention has the advantages that the CNN image identification research technology is used for processing the picture in real time at the edge equipmentside, so that the mass storage and computing pressure of the cloud server is solved.

Description

technical field [0001] The invention relates to a real-time monitoring method and system in the fields of edge computing, cloud computing, image processing and pattern recognition. Background technique [0002] In recent decades, video surveillance system has become an indispensable management method for cities and even countries. Managers can grasp the information on the scene without going to the site in person, reducing the cost of manpower and material resources. Through the monitoring system, the effect of management and supervision can be improved, and the probability of major accidents can be reduced at the same time. However, traditional monitoring still requires a lot of manpower to compare, classify, and identify images for real-time monitoring, resulting in poor accuracy and precision of monitoring results, and at the same time bringing huge pressure on storage devices. [0003] With the rise of machine learning, the application of neural networks has become more...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that the edge device of the video surveillance system cannot process pictures in real time, and the cloud server has massive storage and computing pressure.

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