CRM

This project hosts the code for implementing the CRM algorithm of the 2024 IEEE Transactions on Circuits and Systems for Video Technology paper:

Consistent Representation Mining for Multi-Drone Single Object Tracking
(accepted by IEEE Transactions on Circuits and Systems for Video Technology, DOI: 10.1109/TCSVT.2024.3411301)

Aerial tracking has received growing attention due to its widespread practical applications. However, single-view aerial trackers are still limited by challenges such as severe appearance variations and occlusions. Existing multi-view trackers utilize cross-drone information to solve the above issues, but suffer from overcoming heterogenous differences in this information. In this paper, we propose the novel Transformer-based consistent representation mining (CRM) module to capture invariant target information and suppress the heterogenous differences in cross-drone information. First, CRM divides the heterogenous input into regions and measures semantic relevance by modeling the relations between regions. Then reliable target regions are roughly localized by selecting the top k strongly relevant regions. Next, the global perception is performed on these reliable regions via multi-head sparse self-attention, further enhancing the understanding of the target and suppressing background regions. In particular, CRM, as a plug-and-play module, can be flexibly embedded into different tracking frameworks (CRM-Siam and CRM-DiMP). Besides, the multi-view correction strategy is designed to ensure the timely correction of multi-view information and the full utilization of its own information. Extensive experiments are conducted on the multi-drone dataset, MDOT. The results show that the CRM-assisted trackers effectively improve the accuracy and robustness of the multi-drone tracking system and outperform other outstanding trackers.

• The paper can be downloaded from IEEE Xplore

• [Models and Raw Results]

• [Models and Raw Results (Baidu)]

• [Raw Results for Other Trackers]

• [Json file for CRM-Siam testing]

• The tracking demos are displayed on the Bilibili

Multi-Drone Tracking

Model	MDOT (Suc./Pre.)	Drone1 (Suc./Pre.)	Drone2 (Suc./Pre.)
SiamBAN (baseline)	0.394/0.570	0.426/0.619	0.363/0.521
CRM-Siam	0.401/0.602	0.417/0.627	0.385/0.577
Super-DiMP (baseline)	0.506/0.728	0.536/0.769	0.476/0.687
CRM-DiMP	0.524/0.759	0.535/0.774	0.513/0.743

Acknowledgement

The code based on the PyTracking, PySOT , SiamBAN , Biformer. We would like to express our sincere thanks to the contributors.

Citation:

If you find this work useful for your research, please cite the following papers:

@ARTICLE{10551855,
author={Xue, Yuanliang and Jin, Guodong and Shen, Tao and Tan, Lining and Wang, Nian and Gao, Jing and Wang, Lianfeng},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={Consistent Representation Mining for Multi-Drone Single Object Tracking},
year={2024},
volume={},
number={},
pages={1-1},
keywords={Target tracking;Customer relationship management;Transformers;Drones;Visualization;Lighting;Object tracking;Aerial tracking;multi-drone tracking;consistent representation mining;reliable region selection;sparse self-attention},
doi={10.1109/TCSVT.2024.3411301}}

If you have any questions about this work, please contact with me via [email protected]