Motion segmentation and abnormal behavior detection via behavior clustering

We consider a change detection problem in video surveillance applications and propose busy-idle rates, meaningful and easy to compute features, to characterize the behavior profile of a given pixel. We describe the geometry independence property of these features, and use them to model the typical behavior that is observed in training sequences. Using a small number of samples for each pixel we generate behavior clusters, wherein pixels with similar behavior profiles fall into the same cluster. We then generate probabilistic models corresponding to behavior clusters, and use these models to perform abnormal behavior detection.