Naoufel Werghi

This work addresses a critical limitation in state estimation and localization accuracy when applying the Kalman filter to 3D LiDAR point clouds for multi-object tracking (MOT), as well as the ongoing challenge of selecting appropriate motion models. Existing literature commonly employs constant-motion assumptions (e.g., constant velocity or constant acceleration), which neglect the complex and object-specific motion dynamics observed in real-world scenarios. Consequently, trajectory deviation and imprecise object localization arise, particularly under occlusion. We attribute these limitations to the fixed formulation of the conventional Kalman filter, which fails to account for the variability of motion dynamics as objects navigate their environments. To overcome this limitation, we introduce a novel Kalman filter formulation that explicitly incorporates motion dynamics, enabling the motion model to adapt online to changes in object-specific movement. The proposed approach substantially improves state estimation, localization accuracy, and trajectory prediction compared to the traditional Kalman filter. This improvement is reflected in tracking performance that surpasses recent benchmarks on the KITTI and Waymo Open Dataset (WOD), with gains of $0.56\%$ and $0.81\%$ in higher-order tracking accuracy (HOTA) and multi-object tracking accuracy (MOTA), respectively. Furthermore, the proposed Kalman filter consistently outperforms the baseline across a variety of detectors. It also demonstrates enhanced robustness to long-term occlusions, achieving improvements of $1.22\%$ in HOTA and $1.55\%$ in MOTA on the KITTI dataset. The formulation is computationally efficient, introducing only approximately $0.078$~ms of additional processing time per frame, thereby maintaining real-time applicability.