Bounding-Box Trajectories Matter for Video Anomaly Detection
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....
| Authors | Inpyo Song, Jangwon Lee |
|---|---|
| Primary category | cs.CV |
| Published | 21 May 2026 |
| Last updated at source | 8 September 2026 |
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