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SiamTFA: Siamese Triple-stream Feature Aggregation Network for Efficient RGBT Tracking

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

Citation

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={2024},
  doi={10.1109/TITS.2024.3512551}}

Install the environment

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".

Training

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

Then run:

python ltr/run_training.py

Testing

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

Then run:

python pytracking/run_tracker.py --dataset_name rgbt234

Tracking results

Download the tracking results from Baidu Netdisk code: pm6p

Download the model weights from Baidu Netdisk code: 143t

Acknowledgments

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