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.

朝觐是一年一度的伊斯兰朝圣，每年有数百万朝圣者参加。因此，存在许多安全管理问题。现有的解决方案只能解决小规模人群的问题，其中包含简单的异常行为和清晰的监控视频。然而，对于大规模的人群来说，表演并没有达到令人满意的结果。因此，我们提出了一种基于光流和生成对抗网络（GAN）的异常行为检测方法。本文的主要贡献有三点。首先，基于光流提取模型的动态特征。通过实验验证了特征的有效性。第二，我们提出了一种基于 GAN 的光流框架，并使用迁移学习策略来检测大规模人群场景中的行为异常。该框架使用 U-Net 和 Flownet 来生成和区分海量人群中个体的正常和异常行为。最后，收集并测试了多个来自不同场景的异常行为朝圣视频。UMN场景1、2、3和UCSD的准确率分别达到99.4%、97.1%、97.6%和89.26%。它还使用 Abnormal Behaviors HAJJ 数据集在大规模人群视频中实现了 79.63% 的检测准确率。收集并测试了多个来自不同场景的异常行为朝圣视频。UMN场景1、2、3和UCSD的准确率分别达到99.4%、97.1%、97.6%和89.26%。它还使用 Abnormal Behaviors HAJJ 数据集在大规模人群视频中实现了 79.63% 的检测准确率。收集并测试了多个来自不同场景的异常行为朝圣视频。UMN场景1、2、3和UCSD的准确率分别达到99.4%、97.1%、97.6%和89.26%。它还使用 Abnormal Behaviors HAJJ 数据集在大规模人群视频中实现了 79.63% 的检测准确率。