Abnormal crowd behavior detection based on optical flow and dynamic threshold
Yang Liu, Xiaofeng Li, Limin Jia · 2014
In this paper, we introduce a novel method to detect abnormal crowd activity: crowd running suddenly. This method is based on the whole motion intensity of the crowd which can be obtained by accumulating all optical flow vectors of a frame. Then we can detect the abnormal crowd activity by setting a threshold to detect whether the motion intensity changed suddenly. However, the computation of the optical flow is sensitive to light conditions resulting in much false detection. Based on that, we present a method to set a dynamic threshold related to the unstable optical flow which can adapt to the changing light conditions. Without training process and priori knowledge to set a static threshold, this method can detect the abnormal crowd activity robustly without much computation.