Crowd event detection based on motion vector intersection points

This paper presents an event detection approach in crowd surveillance videos based on motion vector intersection points. It contains three steps: firstly, to extract the local motion vectors by feature tracking. Secondly, to select appropriate pairs of motion vectors and calculate three types of intersection points which represent the spatial character of crowd event. And the final step is to obtain the intersection point clusters by density based clustering, and then to detect the events by searching the most possible candidate and voting. Experimental results show that the presented approach can effectively detect the concurrent events of different densities and within different ranges controlled by parameters. The results also show that the proposed approach is robust to illumination, shadows and noise from event itself.