Abnormal behavior poses a great threat to social security and stability. The resulting violence or crime leads to terrible consequences. How to utilize reasonable means to predict the dangerous intentions of massive crowds and prevent the potential hazard to the public is significant for social security. A crowd monitoring and management system is an effective way to detect abnormal behavior. In this article, we release unmanned aerial vehicles as well as fixed ground devices to achieve multi-level and multi-modal behavioral sensing on a massive crowd, deploy a hybrid model in edge cloud to extract global features from behavioral data of a massive crowd, and then utilize these global features to construct decent classification algorithms for action recognition and behavioral semantic cognition. With the cooperation of behavioral data and cognitive algorithms, we can understand the instantaneous emotions of the crowd. On the basis of behavioral data and the emotional state of the crowd, the correlation between daily behavior and any dangerous intention of a massive crowd has been revealed by utilizing behavioral big data analysis, which is a key foundation for predicting people's dangerous intentions. Finally, we conduct a case study of abnormal behavior detection based on pix2pix and continuous video frames. The experimental results show that the performance of our method is better than other algorithms in both public datasets and the customized Hajj dataset. The proposed novel pattern for the effective learning of a massive crowd is validated to effectively eliminate some of the possible dangers caused by abnormal behavior.

中文翻译：

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面向人群监控系统的基于边缘计算的异常行为学习

异常行为对社会治安和稳定构成极大威胁。由此产生的暴力或犯罪会导致可怕的后果。如何运用合理的手段预测大量人群的危险意图，防范对公众的潜在危害，对社会保障具有重要意义。人群监控和管理系统是检测异常行为的有效方法。在本文中，我们发布无人机和固定地面设备，实现对海量人群的多层次、多模态行为感知，在边缘云中部署混合模型，从海量人群的行为数据中提取全局特征，然后利用这些全局特征构建体面的分类算法，用于动作识别和行为语义认知。通过行为数据和认知算法的配合，我们可以了解人群的瞬时情绪。在行为数据和人群情绪状态的基础上，利用行为大数据分析揭示日常行为与大量人群的任何危险意图之间的相关性，这是预测人们危险意图的关键基础。最后，我们进行了一个基于 pix2pix 和连续视频帧的异常行为检测案例研究。实验结果表明，我们的方法在公共数据集和定制的 Hajj 数据集上的性能都优于其他算法。所提出的大规模人群有效学习的新模式被验证有效地消除了异常行为引起的一些可能的危险。