All-weather video monitoring method based on deep learning

Abstract

The invention discloses an all-weather video monitoring method based on deep learning. The all-weather video monitoring method based on deep learning includes the following steps that video streaming is real-timely collected, and multiple original sampled graph samples and speed sampled graph samples are obtained through line sampling on basis of the obtained video streaming; the obtained speed sampled graph samples are subjected to space-time correction; on basis of original sampled graphs and speed sampled graphs, off-line training is performed to obtain a deep learning model, and the deep learning model comprises a classification model and a statistical model; the real-time video streaming is subjected to crowd state analysis by means of the obtained deep learning model. According to the all-weather video monitoring method based on deep learning, good adaptability can be achieved in terms of different environments, illumination intensities, weather situations and camera angles, high accuracy can be guaranteed in terms of crowding environments such as rushing out of mass flow crowds, the calculated amount is small, requirements of real-time video processing can be met, and the all-weather video monitoring method based on deep learning is widely applicable to monitoring and managing of public places such as buses, subways and squares where stranded people are dense.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to an all-weather video monitoring method based on deep learning, which is especially suitable for analyzing the status of large-flow crowds. Background technique [0002] At present, the level of urbanization in my country has exceeded 50%. The influx of a large number of floating population makes the density of urban population more and more large, and large-scale crowd activities become more and more frequent. It is not uncommon for major accidents to occur due to crowding and trampling. Therefore, how to monitor and manage crowds, and how to actively identify and timely warn of mass incidents in the early stages has become one of the research hotspots in the field of video surveillance in various countries. In order to better identify and warn of mass abnormal events, thereby reducing the occurrence of disasters, it is a key factor to grasp changes in cro...

AI Technical Summary

Problems solved by technology

However, due to different monitoring scenarios, differences in camera installation angles, changes in wea

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