TATrack

The official implementation for paper "A Target-Aware Transformer for Object Tracking".

Installation

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

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

Install pytorch and torchvision.

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

Install other required packages.

pip install -r requirements.txt

Test

Prepare the datasets: OTB2015, VOT2018, UAV123, GOT-10k, TrackingNet, LaSOT, ILSVRC VID*, ILSVRC DET*, COCO*, and something else you want to test. Set the paths as the following:

├── SparseTT
|   ├── ...
|   ├── ...
|   ├── datasets
|   |   ├── COCO -> /opt/data/COCO
|   |   ├── GOT-10k -> /opt/data/GOT-10k
|   |   ├── ILSVRC2015 -> /opt/data/ILSVRC2015
|   |   ├── LaSOT -> /opt/data/LaSOT/LaSOTBenchmark
|   |   ├── OTB
|   |   |   └── OTB2015 -> /opt/data/OTB2015
|   |   ├── TrackingNet -> /opt/data/TrackingNet
|   |   ├── UAV123 -> /opt/data/UAV123/UAV123

Notes

i. Star notation(*): just for training. You can ignore these datasets if you just want to test the tracker.

ii. In this case, we create soft links for every dataset. The real storage location of all datasets is /opt/data/. You can change them according to your situation.

Download the pretrained models.

📎 GOT-10k model 📎 fulldata model
Set the pretrained model path for the item pretrain_model_path in the configuration file, then run shell commands.
Note that all paths we used here are relative, not absolute. See any configuration file in the experiments directory for examples and details.

GOT-10k

python main/test.py --config experiments/tatrck/test/got10k/tatrack_swin_got10k.yaml

LaSOT

python main/test.py --config experiments/tatrck/test/lasot/tatrack_swin_lasot.yaml

TrackingNet

python main/test.py --config experiments/tatrck/test/trackingnet/tatrack_swin_trackingnet.yaml

UAV123

python main/test.py --config experiments/tatrck/test/uav123/tatrack_swin_uav123.yaml

OTB2015

python main/test.py --config experiments/tatrck/test/otb2015/tatrack_swin_otb2015.yaml

Training

Prepare the datasets as described in the last subsection.
Download the pretrained backbone model from here, and put it in the TATrack/models/swin/ directory.
Run the shell command.

GOT-10k

python main/train.py --config experiments/tatrck/train/got10k/tatrack_swin_train_got10k.yaml

fulldata

python main/train.py --config experiments/tatrck/train/fulldata/tatrack_swin_train_fulldata.yaml

Testing Results

Click here to download all testing results that includes:

LaSOT
TrackingNet
GOT-10k
UAV123
OTB2015

Acknowledgement

Repository

This repository is built on the top of the single object tracking framework video_analyst. See it for more instructions and details.

References

Contact

Kai Huang

If you have any questions, just create issues or email me:smile:.

Kevoen/TATrack