This work addresses limitations in recent 3D tracking-by-detection methods, focusing on identifying legitimate trajectories and addressing state estimation drift in Kalman filters. Current methods rely heavily on threshold-based filtering of false positive detections using detection scores to prevent ghost trajectories. However, this approach is inadequate for distant and partially occluded objects, where detection scores tend to drop, potentially leading to false positives exceeding the threshold. Additionally, the literature generally treats detections as precise localizations of objects. Our research reveals that noise in detections impacts localization information, causing trajectory drift for occluded objects and hindering recovery. To this end, we propose a novel online track validity mechanism that temporally distinguishes between legitimate and ghost tracks, along with a multi-stage observational gating process for incoming observations. This mechanism significantly improves tracking performance, with a $6.28\%$ in HOTA and a $17.87\%$ increase in MOTA. We also introduce a refinement to the Kalman filter that enhances noise mitigation in trajectory drift, leading to more robust state estimation for occluded objects. Our framework, RobMOT, outperforms state-of-the-art methods, including deep learning approaches, across various detectors, achieving up to a $4\%$ margin in HOTA and $6\%$ in MOTA. RobMOT excels under challenging conditions, such as prolonged occlusions and tracking distant objects, with up to a 59\% improvement in processing latency.

翻译：本研究针对近期基于检测的三维跟踪方法存在的局限，重点关注合法轨迹的识别与卡尔曼滤波器中状态估计漂移的应对。现有方法严重依赖基于阈值的检测分数过滤来剔除误检，以防止产生虚假轨迹。然而，该方法对于远距离和部分遮挡物体效果不佳，因为此类物体的检测分数往往下降，可能导致误检超过阈值。此外，现有文献通常将检测结果视为物体的精确定位。我们的研究发现，检测中的噪声会影响定位信息，导致被遮挡物体的轨迹漂移并阻碍其恢复。为此，我们提出了一种新颖的在线轨迹有效性判定机制，该机制能够从时间维度上区分合法轨迹与虚假轨迹，并结合一个用于处理新观测的多阶段观测门控过程。该机制显著提升了跟踪性能，在HOTA指标上提升$6.28\%$，在MOTA指标上提升$17.87\%$。我们还对卡尔曼滤波器进行了改进，以增强对轨迹漂移中噪声的抑制，从而为被遮挡物体提供更鲁棒的状态估计。我们的框架RobMOT在多种检测器上均优于现有最先进方法（包括深度学习方法），在HOTA指标上最高提升$4\%$，在MOTA指标上最高提升$6\%$。RobMOT在具有挑战性的条件下表现卓越，例如长时间遮挡和远距离物体跟踪，其处理延迟最高可降低59\%。