  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.
