Papers › An Exploratory Study on Human-Centric Video Anomaly Detection through Variational...
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
Video Anomaly Detection (VAD) represents a challenging and prominent research task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) has drawn considerable attention from the research community due to several inherent advantages over pixel-based approaches despite the occasional suboptimal performance. Specifically, PAD is characterized by reduced computational complexity, intrinsic privacy preservation, and the mitigation of concerns related to discrimination and bias against specific demographic groups. This paper introduces TSGAD, a novel human-centric Two-Stream Graph-Improved Anomaly Detection leveraging Variational Autoencoders (VAEs) and trajectory prediction. TSGAD aims to explore the possibility of utilizing VAEs as a new approach for pose-based human-centric VAD alongside the benefits of trajectory prediction. We demonstrate TSGAD's effectiveness through comprehensive experimentation on benchmark datasets. TSGAD demonstrates comparable results with state-of-the-art methods showcasing the potential of adopting variational autoencoders. This suggests a promising direction for future research endeavors. The code base for this work is available at https://github.com/TeCSAR-UNCC/TSGAD.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | ShanghaiTech | TSGAD | AUC | 80.6% | #18 of 31 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech Campus | TSGAD | AUC-ROC | 80.67 | #1 of 1 | Archive leaderboard | report |
| Video Anomaly Detection | HR-ShanghaiTech | TSGAD | AUC | 81.77 | #3 of 14 | Archive leaderboard | report |
| Video Anomaly Detection | ShanghaiTech | TSGAD | AUC | 80.6% | #5 of 7 | Archive leaderboard | report |
| Video Anomaly Detection | ShanghaiTech Campus | TSGAD | AUC | 80.6 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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