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.

通过对 Kalman 滤波进行改进以减小轨迹漂移噪声，并引入一种新的在线轨迹有效性机制来减少幽灵轨迹，从而在 3D 多物体跟踪中提出了 RobMOT 框架，该框架在各种检测器上表现出优于截至目前最先进方法的卓越性能，包括深度学习方法，在 MOTA 上的边缘为 3.28％，在 HOTA 上的边缘为 2.36％，并且在处理延迟上提高了 59％。