Abstract: Content With the continuous advancement of deep learning technology, abnormal behavior detection has shifted from the traditional machine learning stage to the application of deep learning methods, and the research focus of abnormal behavior detection has shifted from single-person abnormal behavior to multi-person abnormal behavior. Multi-person abnormal behavior detection based on deep learning has become a research hotspot in the field of computer vision. For multi-person abnormal behavior detection, it is necessary to select appropriate feature extraction methods and abnormal behavior detection methods according to different scenarios. In order to make researchers have a clear and systematic understanding of the existing feature extraction methods based on deep learning and abnormal behavior detection methods in multi-person scenarios. This paper systematically analyzes and summarizes the feature extraction methods based on deep learning and multi-person abnormal behavior detection methods, and looks forward to the future development direction in view of the shortcomings of the existing methods. Firstly, the definition, characteristics and classification of multi-person abnormal behavior are given. Secondly, based on the feature extraction method based on deep learning and the multi-person abnormal behavior detection method based on deep learning, the existing multi-person abnormal behavior detection methods are sorted out and summarized. Subsequently, the commonly used public abnormal behavior detection data sets are introduced, and the performance of some models on common public data sets is compared. Finally, the future research directions in this field are prospected.

Key words: Deep Learning, Feature Extraction, Abnormal Behavior Detection, Anormal Behavior of Multiple People, Multimodal

摘要： 随着深度学习技术的不断进步，异常行为检测已经从传统的机器学习阶段转向了深度学习方法的应用，并且异常行为检测的研究焦点从单人异常行为转向多人异常行为。基于深度学习的多人异常行为检测已经成为计算机视觉领域的研究热点。对于多人异常行为检测来说，需要根据场景的不同选择合适的特征提取方法与异常行为检测方法。为了使研究者对现存的基于深度学习的特征提取方法和在多人场景下的异常行为检测方法有清晰而系统的了解。本文对基于深度学习的特征提取方法和多人异常行为检测方法进行系统地分析与总结，并针对现存方法的不足，对未来发展方向进行展望。首先，给出多人异常行为的定义、特点及分类。其次，以基于深度学习的特征提取方法与多人异常行为检测方法为线索，对现有的多人异常行为检测方法进行梳理和归纳。随后，对常用的公共异常行为检测数据集进行介绍，并对部分模型在常用公共数据集上进行性能对比。最后，对本领域未来的研究方向进行展望。

关键词: 深度学习, 特征提取, 异常行为检测, 多人异常行为, 多模态