Anomaly detection via 3D-HOF and fast double sparse representation

This paper presents a framework for anomaly detection in videos which considers both motion and appearance features. For motion cues, we propose a new feature called 3D-HOF, which effectively extracts both velocity and orientation from the optical flow map. At the same time, we introduce the concept of “depth of field” problem to make the detection more accurate when the velocity of an object may seem to be different according to its distance to the camera. For appearance cues, we use 3D gradients of spatio-temporal cuboids as features. Next in the detection part, we propose a fast double sparse representation method in order to make the process faster in some actual scenes. Finally, we integrate both the outcomes of using motion and appearance cues as the final outcomes. Results on discriminative datasets show efficiency and effectiveness compared to the state-of-the-art methods.