Summary of Graph-jigsaw Conditioned Diffusion Model For Skeleton-based Video Anomaly Detection, by Ali Karami et al.
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
by Ali Karami, Thi Kieu Khanh Ho, Narges Armanfard
First submitted to arxiv on: 18 Mar 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 The proposed framework, Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection (GiCiSAD), addresses the challenge of detecting abnormal patterns in videos by combining three novel modules. The first module uses graph attention to capture spatio-temporal dependencies, while the second module solves a jigsaw puzzle to distinguish region-level differences between normal and abnormal motions. The third module generates a wide range of human motions using graph-based conditional diffusion. Experimental results on four datasets show that GiCiSAD outperforms existing methods with fewer training parameters, setting a new state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GiCiSAD is a new way to detect unusual movements in videos. It’s like putting together a puzzle to figure out what’s normal and what’s not. The model uses three parts: one to understand how things move over time, another to find small differences between normal and abnormal actions, and the last part to create many possible human motions. This helps it detect anomalies better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Diffusion » Diffusion model
