基于运动语义的归一化流用于隐私保护视频异常检测
Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection
AI总结：
本文提出MSG-Flow方法，通过分解骨架基视频异常检测任务，利用向量量化变分自编码器、自回归Transformer和条件归一化流，提升多层级运动语义建模能力，实现88.1%和75.8%的AUC性能。
摘要：
随着具身感知系统在交互多媒体应用中日益融合数字与物理领域，对物理环境中人类活动理解的隐私保护方法需求变得至关重要。视频异常检测是此类具身多媒体系统中智能监控和法医分析的关键任务。基于骨架的方法已成为一种隐私保护的替代方案，通过抽象的人体姿态表示处理物理世界信息，同时丢弃敏感的视觉属性如身份和面部特征。然而，现有基于骨架的方法大多以整体方式建模连续运动轨迹，无法捕捉由离散语义原始和细粒度运动细节组成的层次化的人类活动特性，导致在不同抽象层次上出现异常时判别能力下降。为此，我们提出运动语义引导的归一化流（MSG-Flow），将基于骨架的视频异常检测分解为层次化的运动语义建模。它利用向量量化变分自编码器将连续运动离散化为可解释的原始，使用自回归Transformer建模语义层面的时间依赖性，并使用条件归一化流捕捉细节层面的姿态变化。在基准测试（HR-ShanghaiTech & HR-UBnormal）上的广泛实验表明，MSG-Flow在AUC上分别达到88.1%和75.8%的最先进性能。
英文摘要：
As embodied perception systems increasingly bridge digital and physical realms in interactive multimedia applications, the need for privacy-preserving approaches to understand human activities in physical environments has become paramount. Video anomaly detection is a critical task in such embodied multimedia systems for intelligent surveillance and forensic analysis. Skeleton-based approaches have emerged as a privacy-preserving alternative that processes physical world information through abstract human pose representations while discarding sensitive visual attributes such as identity and facial features. However, existing skeleton-based methods predominantly model continuous motion trajectories in a monolithic manner, failing to capture the hierarchical nature of human activities composed of discrete semantic primitives and fine-grained kinematic details, which leads to reduced discriminability when anomalies manifest at different abstraction levels. In this regard, we propose Motion Semantics Guided Normalizing Flow (MSG-Flow) that decomposes skeleton-based VAD into hierarchical motion semantics modeling. It employs vector quantized variational auto-encoder to discretize continuous motion into interpretable primitives, an autoregressive Transformer to model semantic-level temporal dependencies, and a conditional normalizing flow to capture detail-level pose variations. Extensive experiments on benchmarks (HR-ShanghaiTech & HR-UBnormal) demonstrate that MSG-Flow achieves state-of-the-art performance with 88.1% and 75.8% AUC respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
