Summary of Recurrent Aligned Network For Generalized Pedestrian Trajectory Prediction, by Yonghao Dong et al.
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
by Yonghao Dong, Le Wang, Sanping Zhou, Gang Hua, Changyin Sun
First submitted to arxiv on: 9 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 This paper proposes a novel approach to generalize pedestrian trajectory prediction models without accessing target domain data. The challenge lies in the domain shift problem, where previous methods relied on collecting a portion of trajectory data from the target domain. Instead, the authors introduce Recurrent Aligned Network (RAN) to minimize the domain gap through domain alignment. RAN consists of a recurrent alignment module and a pre-aligned representation module, which considers social interactions during the alignment process. The method is evaluated on three widely used benchmarks, demonstrating superior generalization capability compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at predicting where people will walk next. This is important because it can help robots and self-driving cars avoid collisions. Right now, computers are not very good at this because they learn from data that might be different from what they’ll see in real life. The authors came up with a new way to make computers more flexible so they can work well even when the data is different. They tested their idea on three sets of data and it worked really well. |
Keywords
» Artificial intelligence » Alignment » Generalization
