Generative adversarial network based abnormal behavior detection in massive crowd videos: a Hajj case study

Hajj is an annual Islamic pilgrimage, which is attended by millions of pilgrims every year. Therefore, there are many security management problems. The existing solutions can only solve the problems of a small-scale crowd, which contains a simple abnormal behavior and a clear surveillance video. However, the performance hasn’t reached a satisfactory result for a large-scale crowd. Therefore, we propose an abnormal behavior detection method based on optical flow and generative adversarial network (GAN). There are three main contributions in this paper. Firstly, the dynamic features of the model are extracted based on the optical flows. The effectiveness of the features is validated by experiments. Secondly, we propose an optical flow framework based on GAN and use a transfer learning strategy to detect behavioral abnormalities in large-scale crowd scenes. The framework uses U-Net and Flownet to generate and distinguish the normal and abnormal behaviors of individuals within the massive crowds. Finally, a number of abnormal behavior pilgrimage videos from different scenes is collected and tested. The accuracy of UMN scenes 1, 2, 3, and UCSD reaches 99.4%, 97.1%, 97.6% and 89.26%, respectively. It also achieves 79.63% of detection accuracy in the large-scale crowd videos using Abnormal Behaviors HAJJ dataset.