Detection of violent crowd behavior based on statistical characteristics of the optical flow
Jianfeng Huang, Shui-Li Chen · 2014
Detection of violent crowd behavior is an important topic in crowd surveillance. Through a study on optical flow, we can find that when crowd violence occurs, the change of variance on optical flow is become large. Hence, we introduce a statistic method based on optical flow field to detect violent crowd behaviors. Our method considers the statistical characteristics of optical flow field and extracts a statistical characteristic of the optical flow (SCOF) descriptor from these characteristics to represent the sequences of video frames. The SCOF descriptors are then categorized as either normal or violence using linear Support Vector Machine. The experiments are conducted on Crowd Database and Hockey dataset. Experimental results show the SCOF descriptor is easy and can efficiently detect the crowd violence.