Summary of Sparse Prototype Network For Explainable Pedestrian Behavior Prediction, by Yan Feng et al.
Sparse Prototype Network for Explainable Pedestrian Behavior Prediction
by Yan Feng, Alexander Carballo, Kazuya Takeda
First submitted to arxiv on: 16 Oct 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 paper introduces Sparse Prototype Network (SPN), an explainable method for predicting pedestrian behavior. SPN predicts a pedestrian’s future action, trajectory, and pose while providing explanations of its inner workings. The model leverages an intermediate prototype bottleneck layer to generate sample-based explanations. These prototypes are modality-independent, allowing the model to extend to arbitrary combinations of modalities. SPN achieves state-of-the-art performance on action, trajectory, and pose prediction tasks on TITAN and PIE datasets. To evaluate explainability, the paper proposes a metric called Top-K Mono-semanticity Scale. Qualitative results show a positive correlation between sparsity and explainability. The code for SPN is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a deep learning model that can predict what pedestrians will do next, where they’ll go, and how they’ll move. It’s important because it could help with things like self-driving cars and smart cities. The problem with current models is that they don’t explain why they made certain predictions. This new method, called SPN, tries to fix this by showing how the model came up with its answers. SPN works by looking at patterns in the data and using those patterns to make predictions. It does a great job of predicting what pedestrians will do and where they’ll go. |
Keywords
» Artificial intelligence » Deep learning
