Crowd exceptional event detection method based on hypothesis examination

Abstract

The invention relates to a <{EN0}>crowd exceptional event detection method based on hypothesis examination, particularly targeting the crowd of medium density. The crowd exceptional event detection method comprises steps of using an optical flow method to capture crowd motion information so as to obtain crowd motion characteristic descriptor, extracting a characteristic vector of each pixel point from each video frame, adopting a hypothesis examination statistics method to perform classification based on motion characteristic descriptor, and performing comparison on a statistical magnitude value and a threshold value which are obtained by calculation of the hypothesis examination model so as to detect whether the exceptional event happens. The crowd exceptional event detection method not only eliminates the tedious pretreatment step in the feature extraction phase, but also uses the hypothesis examination model in the detection step, which greatly reduces the time complexity and calculation complexity during the detection process on the basis that the detection result is not reduced. The crowd exceptional event detection method not only can be used for global exceptional event detection, but also can be applied to local exceptional event detection.

Description

technical field [0001] The invention relates to a method for detecting abnormal crowd events in public places, in particular to a method for detecting abnormal crowd events based on hypothesis testing. Background technique [0002] In recent years, security issues in public places have become increasingly prominent, and the requirements for intelligent monitoring have also become higher and higher. Therefore, video analysis technology has become a research hotspot in many countries. With the deepening and systematization of video analysis technology research, more and more More problems also emerged. One of the important issues is how to effectively monitor the crowd in public areas. For this reason, various technologies have been developed in various intelligent monitoring related fields. Among them, the development of video-based abnormal crowd event detection technology is particularly rapid. This technology can detect abnormal events in the monitoring area in time, imp...

AI Technical Summary

Problems solved by technology

[0004] Although there are many methods for crowd abnormal event detection, most crowd abnormal event detection algorithms cannot be processed in real time. The main difficulty lies in the time-consuming model detection and high computational complexity.

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