Unsupervised abnormal crowd activity detection using interaction power model

Abnormal event detection in crowded scenes is one of the most challenging tasks in the video surveillance for the public security control. Different from previous work based on learning. We proposed an unsupervised Interaction Power model with an adaptive threshold strategy to detect abnormal group activity by analyzing the steady state of individuals' behaviors in the crowed scene. Firstly, the optical flow field of the potential pedestrians is only calculated within the extracted foreground to reduce the computational cost. Secondly, each pedestrian can be divided into patches of the same size, and the interaction power of the pedestrians will be represented by the motion particles which describe the motion status at the center pixels of the patches. The motion status of each patch is computed by using the optical flows of the pixels within the patch. For each motion particle, its interaction power, defined as its steady state of the current behavior, is computed among all its neighboring motion particles. Finally, the dense crowds' steady state can be represented as a collection of motion particles' interaction power. Here, an adaptive threshold strategy is proposed to detect abnormal events by examining the frame power field which is a fixed-size random sampling of the interaction power of motion particles. Experimental results on the standard UMN dataset and online videos show that our method could detect the crowd anomalies and achieve a higher accuracy compared to the other competitive methods published recently.