Crowd surveillance using Markov Random Fields
Zhi Zhong, Ning Ding, Xinyu Wu, Yangsheng Xu · 2008
Video surveillance in crowd is challenging for public security. This paper focuses on the detection of human abnormal behaviors in crowd. The issue is crucial in some special localities and has been less studied. To achieve this goal, this paper defines a crowd energy based on Markov Random Fields. By using wavelet analysis of the energy curves, the crowd status of the scene is detected. After testing the method in the actual environment in a metro surveillance system, we have obtained a result which shows that the method can be used to deal with crowd modeling and real-time surveillance satisfactorily.