Interacting Multiple Model-based Joint Homography Matrix and Multiple Object State Estimation
<|reference_start|>Interacting Multiple Model-based Joint Homography Matrix and Multiple Object State Estimation: A novel MOT algorithm, IMM Joint Homography State Estimation (IMM-JHSE), is proposed. By jointly modelling the camera projection matrix as part of track state vectors, IMM-JHSE removes the explicit influence of camera motion compensation techniques on predicted track position states, which was 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. In addition to applying an IMM to camera motion, a non-standard IMM approach is applied where bounding-box-based BIoU scores are mixed with ground-plane-based Mahalanobis distances in an IMM-like fashion to perform association only. Finally, IMM-JHSE makes use of dynamic process and measurement noise estimation techniques. IMM-JHSE improves upon related techniques on the DanceTrack and KITTI-car datasets, increasing HOTA by 2.64 and 2.11, respectively, while offering competitive performance on the MOT17, MOT20 and KITTI-pedestrian datasets.<|reference_end|>
@article{claasen2024one,
    title={One Homography is All You Need: IMM-based Joint Homography and Multiple
  Object State Estimation},
    author={Paul Johannes Claasen and Johan Pieter de Villiers},
    journal={arXiv preprint arXiv:2409.02562},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02562},
    primaryClass={cs.CV}
}