Abnormal activity detection in video sequences using learnt probability densities

Video surveillance is concerned with identifying abnormal or unusual activity at a scene. In this paper, we develop stochastic models to characterize the normal activities in a scene. Given video sequences of normal activity, probabilistic models are learnt to describe the normal motion in the scene. For any new video sequences, motion trajectories are extracted and evaluated using these learnt probabilistic models to identify if they are abnormal or not. In this paper, we have employed the commonly used prototype based representation to describe the movement of individual objects. The model parameters are estimated in the maximum-likelihood framework.