Using dynamic Bayesian network for scene modeling and anomaly detection

In this paper, we address the problem of scene modeling for performing video surveillance. The problem consists of using the trajectories, obtained by observing objects in a scene, to construct a scene model that can be used to distinguish a normal and an acceptable behavior from a atypical one. In this regard, the proposed method is divided into a training phase and a testing phase. During the training phase, the input trajectories are used to identify different paths or routes commonly taken by the objects in a scene. Important discriminative features are then extracted from these identified paths to learn a dynamic Bayesian network (DBN). During the testing phase, the learned network is used to classify the incoming trajectories based on their size, location, speed, acceleration, and spatio-temproal curvature characteristics. The proposed method (i) handles trajectories of varying lengths, (ii) automatically detects the number of paths presents in a scene, and (iii) introduces the novel usage of the DBN, which is very intuitive and accurately captures the dynamics of the scene. We show results on four datasets of varying lengths and successfully show results for both path clustering and anomalous behavior detection.