📢 关于本代码库与学术论文的关联 本代码是投稿至期刊 The Visual Computer 的学术论文 “PMFNet: Collaborative Prompt Enhancement and Dynamic Fusion for Robust RGB-Thermal Tracking” 的官方实现。 如果您使用了本代码或其中的部分，请务必引用我们的论文。

@article{pmfnet2025visualcomputer, title={PMFNet: Collaborative Prompt Enhancement and Dynamic Fusion for Robust RGB-Thermal Tracking}, author={Cao, Jie and Feng, Xue and Liang, Haopeng}, journal={The Visual Computer (Submitted)}, year={2025}, note={Under Review. Corresponding author: Haopeng Liang (email: 20240022@lut.edu.cn)} }

Environment Installation

conda create -n tbsi python=3.8
conda activate tbsi
bash install.sh

Project Paths Setup

Run the following command to set paths for this project

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output

After running this command, you can also modify paths by editing these two files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

Data Preparation

Put the tracking datasets in ./data. It should look like:

${PROJECT_ROOT}
  -- data
      -- lasher
          |-- trainingset
          |-- testingset
          |-- trainingsetList.txt
          |-- testingsetList.txt
          ...

Training

Download ImageNet or SOT pretrained weights and put them under $PROJECT_ROOT$/pretrained_models.

python tracking/train.py --script tbsi_track --config vitb_256_tbsi_32x4_4e4_lasher_p061_in1k --save_dir ./output/vitb_256_tbsi_32x4_4e4_lasher_p061_in1k --mode multiple --nproc_per_node 4

Replace --config with the desired model config under experiments/tbsi_track.

Evaluation

Put the checkpoint into $PROJECT_ROOT$/output/config_name/... or modify the checkpoint path in testing code.

python tracking/test.py tbsi_track vitb_256_tbsi_32x4_4e4_lasher_p061_in1k --dataset_name lasher_test --threads 6 --num_gpus 1

python tracking/analysis_results.py --tracker_name tbsi_track --tracker_param vitb_256_tbsi_32x4_4e4_lasher_p061_in1k --dataset_name lasher_test

Results on LasHeR testing set

Model	Backbone	Pretraining	Precision	NormPrec	Success	FPS	Checkpoint	Raw Result
TBSI	ViT-Base	ImageNet	64.3	60.8	51.0	36.2	download	download
TBSI	ViT-Base	SOT	70.5	66.5	56.5	36.2	download	download

Acknowledgments

Our project is developed upon OSTrack. Thanks for their contributions which help us to quickly implement our ideas.

PMFNet: 跨模态RGB-T目标跟踪框架

This code is the official implementation of the paper submitted to The Visual Computer (《视觉计算机》) titled "PMFNet: Collaborative Prompt Enhancement and Dynamic Fusion for Robust RGB-Thermal Tracking".

论文信息

• Journal: The Visual Computer