README.md

CAMO-MOT

This is the official repo release of the paper CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion.

News

• 2022-09-08. CAMO-MOT is released on arXiv🙂.
• 2022-08-04. We rank first among all methods on nuScenes Dataset for Tracking😊.
• 2022-08-03. We rank 4th among all methods on KITTI Dataset for MOT😀.

Results

Multi-object tracking(on nuScenes test set)

Method	AMOTA	AMOTP
CAMO-MOT	0.753	0.472

You can find detailed results on nuScenes test set on this website. Or you can view the accuracy trend of MOT algorithms on this website

Multi-object tracking(on nuScenes val set)

Tracker	AMOTA	AMOTP
CAMO-MOT	0.763	0.527

On nuScenes, we use BEVFusion and FocalConv as our detectors.

Multi-object tracking(on KITTI test)

Category	HOTA (%)	MOTA (%)	MOTP (%)	MT (%)	ML (%)	IDS	FRAG	FP	FN
Car	79.99	90.38	85.00	84.46	7.54	30	156	2337	942
Pedestrian	44.77	52.48	64.50	35.40	25.77	152	1133	8325	2525

You can find detailed results on KITTI test set on this website.

Multi-object tracking(on KITTI val)

Category	HOTA (%)	MOTA (%)	IDS	FP	FN
Car	82.91	91.96	1	302	371
Pedestrian	50.99	64.75	70	2240	1140

On KITTI, we use PointGNN as our detector.

License

CAMO-MOT is released under the MIT license.

Acknowledgement

In the detection part, many thanks to the following open-source projects:

• CenterPoint
• FocalConv
• BEVFusion
• We especially thank Yukang@yukang2017(FocalConv) for his help.

In the tracking part, many thanks to the following open-source projects:

• DEFT
• EagerMOT
• PC3T

Citation

If you find our paper useful for you, please consider cite us by:grin::

@ARTICLE{10164676,
  author={Wang, Li and Zhang, Xinyu and Qin, Wenyuan and Li, Xiaoyu and Gao, Jinghan and Yang, Lei and Li, Zhiwei and Li, Jun and Zhu, Lei and Wang, Hong and Liu, Huaping},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking With Camera-LiDAR Fusion}, 
  year={2023},
  volume={},
  number={},
  pages={1-16},
  doi={10.1109/TITS.2023.3285651}}