A Machine Learning-Based Video Anomaly Detection Method

Abstract

A video anomaly detection method based on machine learning, comprising the following steps: 1) video file reading: read the video file as bmp image data frame by frame; 2) video anomaly detection: detect the decomposed image , the process is as follows: a) the picture is too bright, the picture is too dark; b) the gain is disordered; c) blurred and blocked; d) striped interference and scrolling; e) snowflake interference; f) jitter; g) color cast; h ) freeze; i) blue, black screen; 3) machine learning: compare the similarity of the video judged as abnormal with the same type of misjudged video in the database according to the abnormal code, and judge whether the current video is misjudged. The invention has comprehensive detection, self-learning and improving ability, and high accuracy.

Description

technical field

[0001] The invention relates to the technical fields of image processing, video similarity calculation, machine learning, etc., and the main content is a video anomaly detection method based on machine learning. Background technique

[0002] With the development of our society and the rise of the electronic field, video surveillance systems have been widely used in all walks of life, and are no longer limited to special fields such as public security, finance, banking, transportation, military and ports in the past. Communities, office buildings, hotels, public places, factories, shopping malls, communities, and even homes within easy reach of life have all installed video surveillance systems. However, as the number of monitoring cameras in the application continues to increase and the monitoring time continues to prolong, it is becoming more and more difficult to maintain the monitoring software manually in real time, so the intelligence of the monitoring s...

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