Summary of Situate: Indoor Human Trajectory Prediction Through Geometric Features and Self-supervised Vision Representation, by Luigi Capogrosso et al.
SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation
by Luigi Capogrosso, Andrea Toaiari, Andrea Avogaro, Uzair Khan, Aditya Jivoji, Franco Fummi, Marco Cristani
First submitted to arxiv on: 1 Sep 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 SITUATE, a novel approach for indoor human trajectory prediction, leverages equivariant and invariant geometric features and self-supervised vision representation to model intrinsic symmetries and human movements in indoor spaces. By incorporating spatial-semantic information about the environment, SITUATE achieves state-of-the-art performance on two famous indoor datasets, THÖR and Supermarket, and competitive results in outdoor scenarios, demonstrating better generalization capabilities than outdoor-oriented models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Indoor and outdoor environments have different patterns of human motion due to their scope and typical intentions. While there’s attention on outdoor trajectory forecasting, indoor forecasting is still an underexplored area. SITUATE helps by using geometric features and vision representation to predict where people will go in the future. It works well for both indoor and some outdoor scenarios. |
Keywords
» Artificial intelligence » Attention » Generalization » Self supervised
