Unknown Crowd Event Detection from Phase-Based Statistics

A new approach for unknown event detection in videos with dense motion, such as crowds or dynamic textures, is developed, without requiring the estimation of optical flow, with no prior knowledge about normal or abnormal events, and with no training. The proposed method directly extracts motion statistics from the phase of the video's Fourier transform and detects changes in them, and in the video, by applying sequential statistical change detection theory. Focus is placed on the motion component, as videos of densely moving entities, such as temporal textures and crowds, often have a very similar appearance, but different dynamic features. Experiments with synthetically generated datasets demonstrate the method's operation under various conditions, while experiments on a recently introduced crowd dataset show that it succeeds at detecting new events in videos of crowds, with no training, and no prior knowledge of the location of new events in space and time.