Video Anomaly Detection Methods: a Survey

doi:10.3778/j.issn.1673-9418.2404041

Abstract

Abstract: Video abnormal behavior detection is a hot research topic in computer vision. It involves extracting temporal and spatial features from video content to determine the presence of abnormal events and their types within the video, as well as to localize the regions and time where anomalies occur. This paper systematically reviews and categorizes existing methods for video abnormal behavior detection based on supervised/unsupervised learning. This paper categorizes the supervised methods into methods based on deviation mean calculation and multimodal methods. For unsupervised methods, it summarizes various completely unsupervised approaches. Starting from the current mainstream modeling approaches, this paper gives a detailed explanation of deviation mean calculation methods, summarizes multimodal methods based on the utilization and processing of different modal features, and introduces completely unsupervised methods based on two training approaches. By comparing the network architectures of different models, this paper summarizes the test datasets, use cases, advantages, and limitations of various abnormal behavior detection models. Furthermore, it compares and evaluates models using benchmark datasets and common evaluation standards such as frame-level and pixel-level standards, and conducts intra-class comparisons based on performance results, followed by analysis of the outcomes. Lastly, it explores trends in video abnormal behavior detection through five directions: virtual synthetic datasets, multimodal large models, lightweight models, etc.

Key words: abnormal behavior detection, deep learning, completely unsupervised, multimodal features

摘要： 视频异常行为检测作为计算机视觉的研究热点，通过提取视频内容时间和空间特征，判断视频中是否存在异常事件和事件种类，定位异常发生的区域和时间。以有监督/无监督学习为线索，对现有视频异常行为检测方法进行系统梳理和归纳。在有监督类方法中，细分为基于偏差均值计算方法和基于多模态方法；在无监督类方法中，主要总结了基于完全无监督的多种方法。从当前主流建模思路出发对偏差均值计算方法系统性说明，按照不同模态特征的使用及其处理方式对多模态方法进行阐述和总结，根据两种模型训练方式介绍完全无监督方法。对比了不同模型的网络架构，并归纳总结出各类异常行为检测模型的测试数据集、使用场景、优势和局限性。通过基准数据集以帧级标准和像素级标准等常用评价标准进行了模型比较和性能评估，同时通过不同方法的性能表现进行类内对比，并对结果进行分析总结。通过虚拟合成数据集、多模态大模型和轻量级模型等五个方向探究了视频异常行为检测的发展趋势。

关键词: 异常行为检测, 深度学习, 完全无监督, 多模态特征

WU Peichen, YUAN Lining, GUO Fang, LIU Zhao. Video Anomaly Detection Methods: a Survey[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(12): 3100-3125.

吴沛宸, 袁立宁, 郭放, 刘钊. 视频异常行为检测综述[J]. 计算机科学与探索, 2024, 18(12): 3100-3125.

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