Summary of Hyper-sttn: Social Group-aware Spatial-temporal Transformer Network For Human Trajectory Prediction with Hypergraph Reasoning, by Weizheng Wang et al.
Hyper-STTN: Social Group-aware Spatial-Temporal Transformer Network for Human Trajectory Prediction with Hypergraph Reasoning
by Weizheng Wang, Chaowei Wang, Baijian Yang, Guohua Chen, Byung-Cheol Min
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces Hyper-STTN, a novel approach for predicting crowded intents and trajectories in service robots and autonomous vehicles. By leveraging hypergraph-based spatial-temporal transformer networks, the model captures pair-wise and group-wise interactions between pedestrians, crucial for understanding environmental dynamics in crowded scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study predicts how people move around in crowded areas by using a special kind of artificial intelligence called Hyper-STTN. It helps robots and cars learn how to navigate through crowds safely and efficiently. The model looks at both individual movements (pair-wise interactions) and group behaviors (group-wise interactions) to better understand what’s happening in these busy spaces. |
Keywords
* Artificial intelligence * Transformer
