Fighting detection based on optical flow context histogram

This paper proposes a new feature, optical flow context histogram (OFCH) for detecting abnormal events, especially the fighting violence events from a live camera stream. The optical flow context histogram is a log-polar histogram system which combines the histogram of orientation and magnitude of optical flow together. The human action is represented by using the histogram sequence of orientation and magnitude of optical flow. PCA is adopted to reduce the dimension of the human action representation. Several machine learning methods, including random forest, support vector machine and Bayesnet are employed for sequence classification. The experiments were carried out on the video clips downloaded from the Internet. The results show that the proposed methods work well when using a fixed surveillance camera.