Abnormal behavior detection method based on video monitoring

Abstract

The invention discloses an abnormal behavior detection method based on video monitoring, and the method comprises the steps: quickly and accurately detecting a foreground target object in a video frame image through a YOLOv3 target detection algorithm, removing the impact from background noise, and meeting the real-time requirements of abnormal detection; extracting features of a target object inthe video frame image; firstly, features are clustered; inputting the features into an SVM classifier, obtaining an abnormal score with the highest score as the target object, finally obtaining the highest value in the abnormal scores of all the target objects in the video frame image as the abnormal score of the frame image, carrying out the quick and accurate classification through the SVM classifier, and meeting the real-time requirements. According to the method, the deep learning method and the machine learning method are adopted, the occurrence of the abnormal event can be effectively detected, the requirement of real-time detection can be met, and the accuracy of abnormal event detection is improved.

Description

technical field [0001] The invention belongs to the field of security protection, relates to image and video processing, and deep learning technology, in particular to a method for detecting abnormal behavior based on video surveillance. Background technique [0002] Video detection is one of the most important applications in the field of computer vision. Based on abnormal behavior detection in video surveillance, abnormal events can be monitored in real time. When abnormal events occur, security personnel can respond quickly to reduce accidents. losses and even avoid accidents, which greatly improves the level of social security. Efficient and reliable intelligent video surveillance abnormal behavior detection technology, as an important content of intelligent surveillance security, will receive more and more attention. In the era of big data, relying on manual security monitoring methods can no longer fully and accurately cover the video surveillance network. At the same...

AI Technical Summary

Problems solved by technology

In the era of big data, relying on manual security monitoring methods can no longer fully and accurately cover the video surveillance network. At the same time, traditional abnormal behavior detection algorithms are not only poor in real-time, but also low in accuracy.

[0003] In recent years, there have been many models for abnormal behavior detection. The method based on template matching establishes a set of static templates corresponding to the scene behavior through video images. The template describes the characteristic behavior of the human body. When detecting, the actual video is compared with the static template to detect misbehaves, but the method is sensitive to noise and time intervals

Images