This is the official implementation of Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation .

• The code for CVPR2021 is updated. The old version is still available by git checkout vot2020.
• AlphaRefine is accepted by the CVPR2021
• 🏆 Alpha-Refine wins VOT2020 Real-Time Challenge with EAOMultistart 0.499!
• VOT2020 winner presentation slide has been uploaded.**

git clone https://github.com/MasterBin-IIAU/AlphaRefine.git
cd AlphaRefine

Run the installation script to install all the dependencies. You need to provide the ${conda_install_path} (e.g. ~/anaconda3) and the name ${env_name} for the created conda environment (e.g. alpha).

# install dependencies
bash install.sh ${conda_install_path} ${env_name}
conda activate alpha
python setup.py develop

We provide the models of AlphaRefine here. The AUC and Latency are tested with SiamRPN++ as the base tracker on LaSOT dataset, using a RTX 2080Ti GPU.

We recommend download the model into ltr/checkpoints/ltr/SEx_beta.

When combined with more powerful base trackers, AlphaRefine leads to very competitive tracking systems (e.g. ARDiMP). Following are some of the best performed trackers on LaSOT. More results are present in Performance

In this project, we introduce DiMP50, DiMPsuper, ATOM, ECO, RTMDNet, SiamRPN++ as our base trackers.

DiMP50, DiMPsuper, ATOM, ECO are trackers from PyTracking.

The base tracker models trained using PyTracking can be download from model zoo, download them into pytracking/networks

Or you can run the following script to download the models.

echo "****************** Downloading networks ******************"
mkdir pytracking/networks

echo "****************** DiMP Network ******************"
gdown https://drive.google.com/uc\?id\=1qgachgqks2UGjKx-GdO1qylBDdB1f9KN -O pytracking/networks/dimp50.pth
gdown https://drive.google.com/uc\?id\=1MAjrRJDCbL0DSjUKFyDkUuYS1-cYBNjk -O pytracking/networks/dimp18.pth
gdown https://drive.google.com/open?id=1qDptswis2FxihLRYLVRGDvx6aUoAVVLv -O pytracking/networks/super_dimp.pth

echo "****************** ATOM Network ******************"
gdown https://drive.google.com/uc\?id\=1VNyr-Ds0khjM0zaq6lU-xfY74-iWxBvU -O pytracking/networks/atom_default.pth

echo "****************** ECO Network ******************"
gdown https://drive.google.com/uc\?id\=1aWC4waLv_te-BULoy0k-n_zS-ONms21S -O pytracking/networks/resnet18_vggmconv1.pth

Please refer to external/pysot/README.md for establishing SiamRPN++ and external/RT_MDNet/README.md for establishing RTMDNet.

• We provide the evaluation recipes of LaSOT | GOT-10K | TrackingNet | VOT2020. You can follow these recipes to run the evaluation scripts.

• For some of the testing scripts, the path to the testing sets should be specified in pytracking/evaluation/local.py

  If pytracking/evaluation/local.py is not exist, please run

  python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"
  

  An example of pytracking/evaluation/local.py.example is provided.

The training code is based on Pytracking, thus the training operation is similar.

• Download the Dataset GOT-10K | LaSOT | MS-COCO | ILSVRC-VID | ImageNet-DET | YouTube-VOS | TrackingNet, Saliency

  For more details, you can refer to ltr/README.md

• The path to the training sets should be specified in ltr/admin/local.py

  If the ltr/admin/local.py is not exist, please run

  python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

  An example ltr/admin/local.py.example is also provided.

The training recipes are placed in ltr/train_settings (e.g. ltr/train_settings/SEx_beta/SEcm_r34.py), you can configure the training parameters and Dataloaders.

For a recipe named ltr/train_settings/$sub1/$sub2.py, run the following command to launch the training procedure.

python -m torch.distributed.launch --nproc_per_node=8 \
        run_training_multigpu.py $sub1 $sub2 

The checkpoints will be saved in AlphaRefine/checkpoints/ltr/$sub1/$sub2/SEcmnet_ep00*.pth.tar.

When combined with more powerful base trackers, AlphaRefine leads to very competitive tracking systems (e.g. ARDiMP). For more performance reports, please refer to our paper

• LaSOT

  Tracker	Success Score	Speed (fps)	Paper/Code
  ARDiMP (ours)	0.654	32 (RTX 2080Ti)	Paper/Result
  Siam R-CNN (CVPR20)	0.648	5 (Tesla V100)	Paper/Code
  DimpSuper	0.631	39 (RTX 2080Ti)	Paper/Code
  ARDiMP50 (ours)	0.602	46 (RTX 2080Ti)	Paper/Result
  PrDiMP50 (CVPR20)	0.598	30 (Unkown GPU)	Paper/Code
  LTMU (CVPR20)	0.572	13 (RTX 2080Ti)	Paper/Code
  DiMP50 (ICCV19)	0.568	59 (RTX 2080Ti)	Paper/Code
  Ocean (ECCV20)	0.560	25 (Tesla V100)	Paper/Code
  ARSiamRPN (ours)	0.560	50 (RTX 2080Ti)	Paper/Result
  SiamAttn (CVPR20)	0.560	45 (RTX 2080Ti)	Paper/Code
  SiamFC++GoogLeNet (AAAI20)	0.544	90 (RTX 2080Ti)	Paper/Code
  MAML-FCOS (CVPR20)	0.523	42 (NVIDIA P100)	Paper/Code
  GlobalTrack (AAAI20)	0.521	6 (GTX TitanX)	Paper/Code
  ATOM (CVPR19)	0.515	30 (GTX 1080)	Paper/Code

• This repo is based on Pytracking which is an exellent work.
• Thanks for pysot and RTMDNet from which we borrow the code as base trackers.