Real time anomaly detection in H.264 compressed videos

Real time anomaly detection is the need of the hour for any security applications. In this paper, we have proposed a real-time anomaly detection algorithm by utilizing cues from the motion vectors in H.264/AVC compressed domain. The discussed work is principally motivated by the observation that motion vectors (MVs) exhibit different characteristics during anomaly. We have observed that H.264 motion vector magnitude contains relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. Additionally, we have suggested a hierarchical approach through Motion Pyramid for High Resolution videos to further increase the detection rate. The proposed algorithm has performed extremely well on UMN and Peds Anomaly Detection Video datasets, with a detection speed of >150 and 65-75 frames per sec in respective datasets resulting in more than 200× speedup along with comparable accuracy to pixel domain state-of-the-art algorithms.