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Timely, robust crowd event characterization

The automated analysis of crowd behavior from videos has been a rather challenging problem to address due to the complexity and density of the motion, occlusions and local noise. A novel approach for the fast and reliable detection and characterization of abnormal events in crowd motions is proposed, based on particle advection and accurate optical flow estimation. Experiments on benchmark datasets show that changes are detected reliably and faster than existing methods. Also, regions of change are localized spatially, and the events occurring in the video are characterized with accuracy.