The paper was accepted by the IEEE Transactions on Intelligent Transportation Systems.

If our work is useful for your research, please consider citing:

@ARTICLE{siamtfa,
  author={Zhang, Jianming and Qin, Yu and Fan, Shimeng and Xiao, Zhu and Zhang, Jin},
  journal={IEEE Transactions on Intelligent Transportation Systems}, 
  title={SiamTFA: Siamese Triple-Stream Feature Aggregation Network for Efficient RGBT Tracking}, 
  year={2025},
  volume={26},
  number={2},
  pages={1900-1913},
  doi={10.1109/TITS.2024.3512551}}

Install virtual environment and dependency packages.

conda create -n siamtfa python=3.7
conda activate siamtfa
pip install -r requirements.txt

Create the default environment setting files.

# Environment settings for pytracking. Saved at pytracking/evaluation/local.py
python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"

# Environment settings for ltr. Saved at ltr/admin/local.py
python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

Then set the paths of the project and dataset in "ltr/admin/local.py" and "pytracking/evaluation/local.py".

Set the training parameters in "ltr/train_settings/siamtfa/siamtfa_tracker_settings.py".

Then run:

python ltr/run_training.py

Set the model weight path in "pytracing/parameter/siamtfa/siamtfa.py".

Then run:

python pytracking/run_tracker.py --dataset_name rgbt234

Download the tracking results from Baidu Netdisk code: pm6p

Download the model weights from Baidu Netdisk code: 143t

Thanks for the PyTracking and OSTrack library, which helps us to quickly implement our ideas.