Summary of Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking, by Pranav Singh Chib et al.
Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
by Pranav Singh Chib, Pravendra Singh
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 presents TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates to enhance real-world applicability. The dataset maintains a uniform distribution of missing data within observed trajectories, addressing the challenge of incomplete data due to sensor failure, occlusion, and limited fields of view. The authors examine various imputation methods to reconstruct missing coordinates and benchmark their performance for imputing pedestrian trajectories. Additionally, they evaluate recent trajectory prediction methods on the imputed trajectories, providing valuable insights into the performance of these models. This dataset is a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called TrajImpute that helps predict where people will walk next. Right now, we can only predict this if we have all the information about what someone did before, but in real life, sensors might not always work or there might be things blocking our view. So, they made TrajImpute to make it more like real life, with some missing data. They tested different ways to fill in these gaps and how well different prediction methods work when we have this kind of incomplete information. This new dataset will help scientists develop better systems for self-driving cars or robots that need to understand where people are moving. |
