Video anomaly detection and localization using motion-field shape description and homogeneity testing

Abstract

Detection and localization of abnormal behaviors in surveillance videos of crowded scenes is challenging, where high-density people and various objects performing highly unpredictable motions lead to severe occlusions, making object segmentation and tracking extremely difficult. We associate the optical flows between multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor to describe such short-term trajectories, which reflects faithfully the motion trend and details in local patches. Furthermore, we propose a method to detect anomalies over time and space by judging whether the similarities between the testing sample and the retrieved K-NN samples follow the pattern distribution of homogeneous intra-class similarities, which is unsupervised one-class learning requiring no clustering nor prior assumption. Such a scheme can adapt to the whole scene, since the probability is used to judge and the calculation of probability is not affected by motion distortions arising from perspective distortion, which gains advantage over the existing solutions. We conduct experiments on real-world surveillance videos, and the results demonstrate that the proposed method can reliably detect and locate the abnormal events in video sequences, outperforming the state-of-the-art approaches.