Summary of Spb3dtracker: a Robust Lidar-based Person Tracker For Noisy Environment, by Eunsoo Im et al.
Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment
by Eunsoo Im, Changhyun Jee, Jung Kwon Lee
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 explores LiDAR-based Person Detection and Tracking (PDT) as an alternative to camera-based PDT, driven by growing privacy concerns. Despite the effectiveness of “Tracking-by-Detection” (TBD), LiDAR-based PDT has yet to match the performance of camera-based approaches. The authors examine key components of the LiDAR-based PDT framework, including detection post-processing, data association, motion modeling, and lifecycle management. They introduce SpbTrack, a robust person tracker designed for diverse environments, achieving superior performance on noisy datasets and state-of-the-art results on KITTI Dataset benchmarks and custom office indoor dataset among LiDAR-based trackers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding people in 3D space using special sensors called LiDAR. This technology is being used more because it’s better for privacy than cameras. The researchers looked at how to make this work well, and they came up with a new way to track people that’s really good. They tested it on some datasets and found that it was one of the best methods. |
Keywords
» Artificial intelligence » Tracking
