Summary of An Exploratory Study on Human-centric Video Anomaly Detection Through Variational Autoencoders and Trajectory Prediction, by Ghazal Alinezhad Noghre et al.
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
by Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces TSGAD, a novel approach to pose-based video anomaly detection that leverages variational autoencoders (VAEs) and trajectory prediction. The proposed method, which is human-centric and two-stream in nature, aims to reduce computational complexity while preserving privacy and mitigating bias concerns. By comparing TSGAD’s performance with state-of-the-art methods on benchmark datasets, the authors demonstrate its effectiveness and potential for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TSGAD uses VAEs as a new approach for pose-based human-centric video anomaly detection, which is different from pixel-based approaches. The method also includes trajectory prediction to reduce computational complexity. This makes it a promising direction for future research. The paper shows that TSGAD works well and is comparable to other methods. |
Keywords
» Artificial intelligence » Anomaly detection
