The KITTI Vision Benchmark Suite

This work addresses the critical lack of precision
in state estimation in the Kalman filter for 3D
multi-object tracking (MOT) and the ongoing
challenge of selecting the appropriate motion
model. Existing literature commonly relies on
constant motion models for estimating the states
of objects, neglecting the complex motion dynamics
unique to each object. Consequently, trajectory
division and imprecise object localization arise,
especially under occlusion conditions. The core of
these challenges lies in the limitations of the
current Kalman filter formulation, which fails to
account for the variability of motion dynamics as
objects navigate their environments. This work
introduces a novel formulation of the Kalman
filter that incorporates motion dynamics, allowing
the motion model to adaptively adjust according to
changes in the object's movement.

N/A

@misc{nagy2025accuratestateestimationkalman,
title={Towards Accurate State Estimation:
Kalman Filter Incorporating Motion Dynamics for 3D
Multi-Object Tracking},
author={Mohamed Nagy and Naoufel Werghi and
Bilal Hassan and Jorge Dias and Majid Khonji},
year={2025},
eprint={2505.07254},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.07254},
}