CSWinTT

The official implementation of the CVPR 2022 paper Transformer Tracking with Cyclic Shifting Window Attention

[Models and Raw results] (Google Driver) or [Models and Raw results] (Baidu Driver: bsa2).

Highlights

Introduction

CSWinTT is a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object.

Performance

Tracker	UAV123 (AUC)	LaSOT (NP)	TrackingNet (NP)	GOT-10K (AO)
CSWinTT	70.5	75.2	86.7	69.4

Install the environment

conda create -n cswintt python=3.7
conda activate cswintt
bash install.sh

Data Preparation

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

${CSWinTT_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- trackingnet
         |-- TRAIN_0
         |-- TRAIN_1
         ...
         |-- TRAIN_11
         |-- TEST

Run the following command to set paths for this project

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

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

Train CSWinTT

python tracking/train.py --script cswintt --config baseline_cs --save_dir . --mode single 
python tracking/train.py --script cswintt_cls --config baseline_cs --save_dir . --mode single --script_prv cswintt --config_prv baseline_cs

Test CSWinTT

Download the model and put it in output/checkpoints

UAV123

python tracking/test.py cswintt baseline_cs --dataset uav --threads 32

LaSOT

python tracking/test.py cswintt baseline_cs --dataset lasot --threads 32

GOT10K-test

python tracking/test.py cswintt baseline_got10k_only --dataset got10k_test --threads 32

TrackingNet

python tracking/test.py cswintt baseline_cs --dataset trackingnet --threads 32

Model Zoo and raw results

The trained models and the raw tracking results are provided in the [Models and Raw results] (Google Driver) or [Models and Raw results] (Baidu Driver: bsa2).

Contact

Zikai Song: skyesong@hust.edu.cn

Acknowledgments

Thanks for the PyTracking Library and STARK Library, which helps us to quickly implement our ideas.

SkyeSong38/CSWinTT