Anomaly Detection through Spatio-temporal Context Modeling in Crowded Scenes

A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering the fact that mutual interference of several human body parts potentially happen in the same block, we propose an atomic motion pattern representation using the Gaussian Mixture Model (GMM) to distinguish the motions inside each block in a refined way. Usual motion patterns can thus be defined as a certain type of steady motion activities appearing at specific scene positions. During the second stage, we further use the Markov Random Field (MRF) model to characterize the joint label distributions over all the adjacent local motion patterns inside the same crowded scene, aiming at modeling the severely occluded situations in a crowded scene accurately. By combining the determinations from the two stages, a weighted scheme is proposed to automatically detect anomaly events from crowded scenes. The experimental results on several different outdoor and indoor crowded scenes illustrate the effectiveness of the proposed algorithm.