用包围框轨迹提升视频异常检测，效果超越传统人体姿态方法。
Bounding-Box Trajectories Matter for Video Anomaly Detection
- 用归一化流建模多类包围框轨迹，学习正常运动模式。
- 仅用轨迹的版本在ShanghaiTech上达87.7% AP，超越所有姿态方法。
- 轨迹信息被证明是有效但未被充分挖掘的异常检测线索。
视频异常检测对公共安全至关重要，但因外观、视角和场景动态变化大而仍具挑战性。现有方法中，人体姿态类方法表现优异，因许多数据集中的异常涉及人类，且姿态表示对外观变化鲁棒、运动描述紧凑。然而这些方法常忽略包围框轨迹，尽管其在姿态流程中天然可得。本文提出TrajVAD框架，利用归一化流建模多类包围框轨迹，学习正常运动模式。其仅使用轨迹的变体（TrajVAD-T）无需姿态估计，在ShanghaiTech上达到87.7% AP，超越所有对比的姿态方法；扩展版（TrajVAD-P）融合姿态信息后，在ShanghaiTech上进一步提升至88.6% AUROC和90.9% AP，验证了包围框轨迹作为有效且未被充分探索的模态的价值。
原文摘要 · Abstract (English)
Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in appearance, viewpoint, and scene dynamics. Among existing approaches, human pose-based methods have emerged as a major line of research, showing strong performance since many anomalies in public datasets involve humans and pose representations are robust to appearance changes while providing compact motion descriptions. However, these methods often overlook bounding-box trajectories, although such information is inherently available in pose-based pipelines. In this paper, we explicitly leverage these trajectories as a primary anomaly cue. We present TrajVAD, a framework that models multi-class bounding-box trajectories using normalizing flows to learn normal kinematic patterns. Its trajectory-only variant (TrajVAD-T) eliminates pose estimation and surpasses all compared pose-based methods on ShanghaiTech in AP (87.7%), while achieving the best results on MSAD. An extended version (TrajVAD-P) incorporates pose information and further improves performance to 88.6% AUROC and 90.9% AP on ShanghaiTech, highlighting bounding-box trajectories as an effective yet underexplored modality for video anomaly detection.
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