Current approaches for abnormal event detection in video surveillance either based solely on object trajectories, or seek global changes in scene content as representations for detection. A limitation of trajectory-based approaches is that they depend on the existence of reliable methods for tracking moving objects, and the drawback of frame-based methods is that feature signal computed globally might not be discriminative enough to identify certain events. In this study, we propose a framework for abnormality detection using both descriptive trajectory features and robust frame features. The aggregate feature set contains rich and stable information for describing motion events in a video segment. We show the performance of the proposed framework on common video surveillance applications.
