Summary of Socially-informed Reconstruction For Pedestrian Trajectory Forecasting, by Haleh Damirchi et al.
Socially-Informed Reconstruction for Pedestrian Trajectory Forecasting
by Haleh Damirchi, Ali Etemad, Michael Greenspan
First submitted to arxiv on: 5 Dec 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 model uses a conditional variational autoencoder-based trajectory forecasting module with a reconstructor to learn socially-informed representations for pedestrian trajectory prediction. The module generates pseudo-trajectories, which are used as augmentations during training, and a novel social loss is introduced to guide the model towards stable trajectory forecasting. Experimental results demonstrate strong performances compared to state-of-the-art methods on the ETH/UCY and SDD benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps autonomous systems better predict where people will walk. It’s hard because pedestrians interact with each other, so you need to understand not just where they’ve been but also what’s happening around them. The researchers developed a new model that combines two techniques: one that generates fake trajectories and another that makes the model pay attention to social interactions. They tested it on two big datasets and showed that their approach works better than others. |
Keywords
» Artificial intelligence » Attention » Variational autoencoder
