基于深度学习的监控视频异常检测方法综述  

Survey of anomaly detection methods in surveillance videos based on deep learning

作　　者：汪洋[1,2] 周脚根 严俊 关佶红[1] Wang Yang;Zhou Jiaogen;Yan Jun;Guan Jihong(School of Computer Science and Technology National,Tongji University,Shanghai 201804,China;School of Geography and Planning,Huaiyin Normal University,Huaian 223001,China)

机构地区：[1]同济大学计算机科学与技术学院,上海201804 [2]淮阴师范学院地理科学与规划学院,淮安223001

出　　处：《中国图象图形学报》2025年第3期615-640,共26页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2021YFC3300304);国家自然科学基金项目(62172300,62372326)。

摘　　要：利用监控视频监测异常在社会治理中具有至关重要的地位,因此视频异常检测一直是计算机视觉领域备受关注且具有挑战性的议题。鉴于此,以深度学习的视角,对当前关键的视频异常检测方法进行了分类和综述。首先,全面介绍了视频异常的定义,包括异常的划定和类型分类;随后,分析了目前全监督、弱监督、无监督等方面的深度学习方法在视频异常检测领域的进展,探讨了各自的优缺点,特别针对结合大模型的最新研究进展进行了探讨;接着,详细介绍了常见和最新的数据集,并对它们的特点进行了比较分析和截图展示;最后,介绍了多种异常判定和性能评估标准,对各算法的性能表现进行了对比分析。根据这些信息,本文展望了未来数据集、评估标准以及方法研究的可能发展方向,特别强调了大模型在视频异常检测中的新机遇。综上,本文对于深化读者对视频异常检测领域的理解,以及指导未来的研究方向具有积极意义。Video anomaly detection plays a crucial role in social governance by utilizing surveillance footage,making it a crucial and challenging topic within the field of computer vision.This paper presents a detailed classification and review of current key video anomaly detection methods from a deep learning perspective,analyzing existing technical challenges and future development trends.First,the paper provides a comprehensive introduction to the definition of video anomalies,including the delineation of anomalies and video anomalies,the five types of video anomalies(intuitive anomalies,action change anomalies,trajectory change anomalies,group change anomalies,and spatiotemporal anomalies),and the three characteristics of anomaly detection(abstraction,uncertainty,and sparsity).The paper then reviews the development trends in video anomaly detection research from 2008 to the present based on the digital bibliography&library project(DBLP)database and provides a detailed analysis of the progress of fully supervised,weakly supervised,and unsupervised deep learning methods in the field of video anomaly detection.The core innovations,structures,and advantages and disadvantages of each method are discussed,particularly focusing on the latest research advancements involving large models.For instance,some studies address the challenge of applying virtual anomaly video datasets to real-world scenarios by designing anomaly prompts that guide mapping networks to generate unseen anomalies in real-world settings.Additionally,some works have designed dual-branch model structures based on multimodal large model frameworks.One branch uses the contrastive language-image pre-training(CLIP)visual encoding module for coarse-grained binary classification,while the other branch aligns textual features of anomaly category labels with visual encoding features for fine-grained anomaly classification,surpassing the current state-of-the-art performance in video anomaly detection.Furthermore,research has explored the potential of using GPT-4V,a p

关 键 词：视频异常检测 深度学习 数据集 大模型 监督学习 弱监督学习 无监督学习 多模态 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]