Deep Learning and One-class SVM based Anomalous Crowd Detection

Anomalous event detection in videos is an important and challenging task. This paper proposes a deep representation approach to the problem, which extracts and represents features in an unsupervised way. This algorithm can detect anomalous activity like standing statically and loitering among a crowd of people. Our proposed framework is a two-channel scheme by using feature channels extracted from the appearance and foreground of the original video. Two hybrid deep learning architectures SDAE-DBN-PSVM (a four-layer Stacked Denoising Auto-encoder with three-layer Deep Belief Nets and Plane-based one class SVM) are implemented for these two channels to learn the high-level feature representation automatically and produce two anomaly scores. Finally, a fusion scheme is proposed for combining anomaly scores and detecting anomalous events. Experimental results on a large real-world dataset (MCG) and two benchmark datasets (UCSD and Subway) demonstrate the effectiveness of this approach. Furthermore, quantitative analyses of the effects of the amount of training data and the illumination conditions of the video on the accuracy of anomaly detection are presented.