TCM for RGB-T Tracking

Environment Installation

conda create -n tbsi python=3.8
conda activate tcm
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 tcm_track --config vitb_256_tcm_32x4_4e4_lasher_15ep_in1k --save_dir ./output/vitb_256_tcm_32x4_4e4_lasher_15ep_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 tcm_track vitb_256_tcm_32x4_4e4_lasher_15ep_in1k --dataset_name lasher_test --threads 6 --num_gpus 1

python tracking/analysis_results.py --tracker_name tcm_track --tracker_param vitb_256_tcm_32x4_4e4_lasher_15ep_in1k --dataset_name lasher_test

Acknowledgments

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