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.

翻译：在自动驾驶领域，基于二维摄像头的行人检测与跟踪技术已取得显著进展，促使相关算法得到广泛应用。然而，日益增长的隐私担忧近期已成为重要问题，推动基于激光雷达的行人检测与跟踪成为可行的替代方案。在该领域中，"基于检测的跟踪"已成为主流方法。尽管其具有有效性，基于激光雷达的行人检测与跟踪尚未达到与基于摄像头方法同等的性能水平。本文系统研究了基于激光雷达的行人检测与跟踪框架的关键组成部分，包括检测后处理、数据关联、运动建模与生命周期管理。基于这些分析，我们提出了SpbTrack——一种适用于多样化环境的鲁棒行人跟踪器。我们的方法在噪声数据集上实现了卓越性能，并在KITTI数据集基准及自定义办公室室内数据集中取得了基于激光雷达跟踪器的最先进结果。