Summary of Oostraj: Out-of-sight Trajectory Prediction with Vision-positioning Denoising, by Haichao Zhang et al.
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising
by Haichao Zhang, Yi Xu, Hongsheng Lu, Takayuki Shimizu, Yun Fu
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 addresses the critical issue of trajectory prediction in computer vision and autonomous driving, particularly for understanding pedestrian behavior. Existing approaches often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and noisy sensor data. The authors present a novel method that leverages a vision-positioning technique to denoise noisy sensor observations and precisely map sensor-based trajectories of out-of-sight objects into visual trajectories. This approach demonstrates state-of-the-art performance on the Vi-Fi and JRDB datasets, enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to improve how computers predict what people will do next when they are not in sight. Right now, computers don’t do very well with this because they only have incomplete information about what’s going on. The authors come up with a new way to fix this problem by using special computer vision techniques to clean up the data and make more accurate predictions. This is important for making self-driving cars safer. |
