Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes

摘要

Anomaly detection and location in crowded scenes have attracted a lot of attention in computer vision research community recently due to the increased applications of intelligent surveillance improve security in public. We propose a novel parallel spatial-temporal convolution neural networks model to detect and localize the abnormal behavior in video surveillance. Our approach contains two main steps. Firstly, considering the typical position of camera and the large number of background information, we introduce a novel spatial-temporal cuboid of interest detection method with varied-size cell structure and optical flow algorithm. Then, we use the parallel 3D convolution neural networks to describe the same behavior in different temporal-lengths. That step ensures that the most of behavior information in cuboids could be captured, also insures the reduction of information unrelated to the major behavior. The evaluation results on benchmark datasets show the superiority of our method compared to the state-of-the-art methods. (C) 2020 Elsevier Inc. All rights reserved.