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. Project page at anonymous.

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，推动了相关算法的广泛应用。然而，日益增长的隐私担忧近期已成为主要问题，促使业界转向将基于激光雷达的行人检测与跟踪视为可行的替代方案。在该领域中，“检测跟踪一体化”已成为主流方法。尽管其效果显著，基于激光雷达的行人检测与跟踪在性能上尚未达到基于摄像头系统的同等水平。本文深入分析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——一种专为多样化环境设计的鲁棒性行人跟踪器。我们的方法在噪声数据集上实现了卓越性能，并在KITTI数据集基准测试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中的最优结果。项目页面位于anonymous。