This work addresses the inherited limitations in the current state-of-the-art 3D multi-object tracking (MOT) methods that follow the tracking-by-detection paradigm, notably trajectory estimation drift for long-occluded objects in LiDAR point cloud streams acquired by autonomous cars. In addition, the absence of adequate track legitimacy verification results in ghost track accumulation. To tackle these issues, we introduce a two-fold innovation. Firstly, we propose refinement in Kalman filter that enhances trajectory drift noise mitigation, resulting in more robust state estimation for occluded objects. Secondly, we propose a novel online track validity mechanism to distinguish between legitimate and ghost tracks combined with a multi-stage observational gating process for incoming observations. This mechanism substantially reduces ghost tracks by up to 80\% and improves HOTA by 7\%. Accordingly, we propose an online 3D MOT framework, RobMOT, that demonstrates superior performance over the top-performing state-of-the-art methods, including deep learning approaches, across various detectors with up to 3.28\% margin in MOTA and 2.36\% in HOTA. RobMOT excels under challenging conditions, such as prolonged occlusions and the tracking of distant objects, with up to 59\% enhancement in processing latency.

翻译：摘要：本文针对当前基于检测跟踪范式的先进3D多目标跟踪（MOT）方法中固有的局限性，特别是自动驾驶汽车获取的LiDAR点云流中长期遮挡目标的轨迹估计漂移问题。此外，缺乏充分的轨迹合法性验证会导致鬼影轨迹累积。为解决这些问题，我们提出双重创新：首先，提出卡尔曼滤波的改进方法，增强轨迹漂移噪声抑制能力，从而对遮挡目标实现更鲁棒的状态估计；其次，提出一种新颖的在线轨迹有效性机制，结合多阶段观测门控处理，可区分合法轨迹与鬼影轨迹。该机制能减少高达80%的鬼影轨迹，并将HOTA指标提升7%。据此，我们提出在线3D MOT框架RobMOT，在多种检测器上相比包括深度学习方法在内的最优方法展现出优越性能，其中MOTA指标提升达3.28%，HOTA指标提升达2.36%。在长期遮挡与远距离目标追踪等挑战性场景下，RobMOT的处理延迟最高可降低59%。