CAMOT: Camera Angle-aware Multi-Object Tracking
<|reference_start|>CAMOT: Camera Angle-aware Multi-Object Tracking: This paper proposes CAMOT, a simple camera angle estimator for multi-object tracking to tackle two problems: 1) occlusion and 2) inaccurate distance estimation in the depth direction. Under the assumption that multiple objects are located on a flat plane in each video frame, CAMOT estimates the camera angle using object detection. In addition, it gives the depth of each object, enabling pseudo-3D MOT. We evaluated its performance by adding it to various 2D MOT methods on the MOT17 and MOT20 datasets and confirmed its effectiveness. Applying CAMOT to ByteTrack, we obtained 63.8% HOTA, 80.6% MOTA, and 78.5% IDF1 in MOT17, which are state-of-the-art results. Its computational cost is significantly lower than the existing deep-learning-based depth estimators for tracking.<|reference_end|>
@article{limanta2024camot:,
    title={CAMOT: Camera Angle-aware Multi-Object Tracking},
    author={Felix Limanta, Kuniaki Uto, Koichi Shinoda},
    journal={WACV 2024},
    year={2024},
    doi={10.1109/WACV57701.2024.00635},
    archivePrefix={arXiv},
    eprint={2409.17533},
    primaryClass={cs.CV}
}