Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment
<|reference_start|>Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment: Person detection and tracking (PDT) has seen significant advancements with 2D camera-based systems in the autonomous vehicle field, leading to widespread adoption of these algorithms. However, growing privacy concerns have recently emerged as a major issue, prompting a shift towards LiDAR-based PDT as a viable alternative. Within this domain, "Tracking-by-Detection" (TBD) has become a prominent methodology. Despite its effectiveness, LiDAR-based PDT has not yet achieved the same level of performance as camera-based PDT. This paper examines key components of the LiDAR-based PDT framework, including detection post-processing, data association, motion modeling, and lifecycle management. Building upon these insights, we introduce SpbTrack, a robust person tracker designed for diverse environments. Our method achieves superior performance on noisy datasets and state-of-the-art results on KITTI Dataset benchmarks and custom office indoor dataset among LiDAR-based trackers.<|reference_end|>
@article{im2024spb3dtracker:,
    title={Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment},
    author={Eunsoo Im, Changhyun Jee, Jung Kwon Lee},
    journal={arXiv preprint arXiv:2408.05940},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05940},
    primaryClass={cs.CV cs.AI cs.RO}
}