Method and apparatus for crowd behavior analysis in video monitoring

Abstract

The invention is suitable for video monitoring technologies and relates to a method and an apparatus for crowd behavior analysis in video monitoring. The method comprises: obtaining a video stream and dividing each frame of image in the video stream into image sub-blocks with different perspective degrees by taking human height as a size reference by row; extracting deep neural network features in the image sub-blocks; classifying and optimizing the deep neural network features, obtaining a corresponding vector feature set, establishing SVR mathematical models of different kernel functions according to the vector feature set, and making statistics on the number of people in crowds with different density; and adopting a camera calibration method for obtaining coordinate vectors of the deep neural network features, comparing displacements of the deep neural network features of the two continuous frames of image sub-blocks, and calculating a moving direction and a moving speed of the crowd according to the displacement. According to the method and the apparatus, the crowd in the video monitoring is tracked and analyzed to obtain feature information, such as the moving speed, the moving direction, the number of people and the like, of the crowd, the feature information and historical data are analyzed and compared, a state of the crowd is judged, and an alarm is given for an abnormal event.

Description

technical field [0001] The invention belongs to the technical field of video surveillance, and in particular relates to a method and device for analyzing crowd behavior in video surveillance. Background technique [0002] Video surveillance is an important part of the security system, and it is a comprehensive system with strong defense capabilities. Video surveillance is widely used in many occasions because of its intuitive, accurate, timely and rich information content. In recent years, with the rapid development of computer, network and image processing and transmission technology, video surveillance technology has also made great progress. It is of great significance to analyze the "overall characteristics of the crowd" through video surveillance, realize real-time monitoring and early warning of "abnormal crowd gathering" and "special crowd characteristics", and timely detect crowd congestion and prevent mass events and illegal assemblies. It is an important means to...

AI Technical Summary

Problems solved by technology

The reason is that when video surveillance is recorded, different degrees of crowd crowding will occur in different time periods with different time periods. The existing monitoring methods are not suitable for both low-density crowds and high-density crowds. Behavioral analysis , at the same time, the characteristic information of the crowd cannot be accurately obtained through video surveillance

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