An efficient anomaly detection approach in surveillance video based on oriented GMM

Abstract

The detection and localization of abnormal activities are considered in this work. An efficient approach called oriented G-MM(OGMM) is proposed. The approach uses optical flow as low-level feature and quantizes the orientation of optical flow into 8 sections. In training stage, the approach will learn a GMM model at each orientation section and each position. In testing stage, the proposed approach estimates the probability of whether a position is abnormal using likelihood method. The proposed approach is a local method and can detect and locate anomaly. What's more, in the proposed approach, the same process is done to each position with little interaction between different positions. This makes the approach suit for parallel computing and can deal with large-scale tasks in Big Data times. The experiments verify that the proposed approach is efficient and effective.