TCT+

Intended for the storage of TCT+ code, the repository will be updated after the publication of the paper.

1. Environment setup

Prepare Anaconda, CUDA and the corresponding toolkits. CUDA version required: 11.3.
Create a new conda environment and activate it.

conda create -n TCTPlus python=3.7 -y
conda activate TCTPlus

conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# pytorch version: >= 1.9.0

pip install -r requirements.txt

Download the datasets：

Note: train_dataset/dataset_name/readme.md has listed detailed operations about how to generate training datasets.

To train the TCT+ and model, run train.py with the desired configs:

cd TCTPlus-main
python ./tools/train_tctrack.py

The model will be output in snapshot directory.

Prepare your trained model and put it into tools/snapshot directory. Download testing datasets and put them into test_dataset directory.

python ./tools/test.py --dataset DTB70 --tracker_name TCTPlus --snapshot tools/snapshot/tctPlus.pth

The testing result will be saved in the results/dataset_name/tracker_name directory.

Note: The results of TCT+ will be made public after the publication of the paper.

If you want to evaluate the results of our tracker, please put those results into results directory.

python ./tools/eval.py --tracker_path ./results --dataset DTB70 --tracker_prefix TCTPlus