The KITTI Vision Benchmark Suite

By modelling the camera projection matrix as part
of track state vectors, JHIT removes the explicit
influence of camera motion compensation techniques
on predicted track position states which is
prevalent in previous approaches. Expanding upon
this, static and dynamic camera motion models are
combined through the use of an IMM filter. A
simple bounding box motion model is used to
predict bounding box positions to incorporate
image plane information. JHIT dynamically mixes
bounding-box-based BIoU scores with ground-plane-
based Mahalanobis distances in an IMM-like fashion
to perform association. Finally, JHIT makes use of
dynamic process and measurement noise estimation
techniques.

\sigma_x=14.843421875000002, \sigma_y=16.249625,
\alpha_1=0.83375, \alpha_2=0.5, d_{conf}=0.5,
d_{high}=0.29999999999999993, \Omega=100.0, b=0.0,
\alpha_3=0.9065624999999999, p_{I,I}=0.9,
p_{G,G}=0.9, p_{s,s}=0.71625, p_{d,d}=0.71625

@misc{claasen2024interactingmultiplemodelbasedjoin
t,
title={Interacting Multiple Model-based
Joint Homography Matrix and Multiple Object State
Estimation},
author={Paul Johannes Claasen and Johan
Pieter de Villiers},
year={2024},
eprint={2409.02562},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2409.02562},
}