Summary of Human-centric Video Anomaly Detection Through Spatio-temporal Pose Tokenization and Transformer, by Ghazal Alinezhad Noghre et al.
Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer
by Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
First submitted to arxiv on: 27 Aug 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 In this paper, researchers present a novel approach to Video Anomaly Detection (VAD) that leverages human-centric pose-based features to mitigate privacy concerns and improve detection accuracy. The proposed architecture, SPARTA, is a transformer-based model designed specifically for human-centric pose-based VAD. It utilizes an innovative tokenization method called Spatio-Temporal Pose and Relative Pose (ST-PRP), which produces an enriched representation of human motion over time. This approach allows the attention mechanism to capture both spatial and temporal patterns simultaneously, rather than focusing on only one aspect. The model’s core is a novel Unified Encoder Twin Decoders (UETD) transformer that improves detection accuracy. Extensive evaluations across multiple benchmark datasets demonstrate that SPARTA consistently outperforms existing methods, establishing a new state-of-the-art in pose-based VAD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video Anomaly Detection (VAD) is like trying to find a needle in a haystack. The problem is even harder when we’re looking for unusual human behavior in videos. To solve this challenge, researchers created a special kind of AI model called SPARTA. It’s really good at finding anomalies in video data by paying attention to how people move and behave. What makes SPARTA unique is that it uses information about the pose (position) of the person, which helps keep personal details private and reduces distractions from the background. This new approach is better than other methods at detecting unusual behavior in videos. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Encoder » Tokenization » Transformer
