The KITTI Vision Benchmark Suite

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

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@misc{nagy2024robmot,
title={RobMOT: Robust 3D Multi-Object
Tracking by Observational Noise and State
Estimation Drift Mitigation on LiDAR PointCloud},
author={Mohamed Nagy and Naoufel Werghi and
Bilal Hassan and Jorge Dias and Majid Khonji},
year={2024},
eprint={2405.11536},
archivePrefix={arXiv},
primaryClass={cs.CV}
}