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Alpha-Refine

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

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Setup Alpha-Refine

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.

TrackerBackboneLatencyAUC(%)Model
AR34<sub>c+m</sub>ResNet345.1ms55.9google/baidu[key:jl1m]
AR18<sub>c+m</sub>ResNet184.2ms55.0google/baidu[key:83ef]

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. Results are present in Performance

We provide a concise demo.py as an example for applying alpha refine to dimp. We recommend you should take this script as the starting point of exploring our project. You may need doc/Reproduce.md for setting up the base trackers of our experiments.

How to apply Alpha-Refine to Your Own Tracker

We provide a concise demo.py as an example for applying alpha refine to dimp.

How to Train Alpha-Refine

Please refer to doc/TRAIN.md for the guidance of training Alpha-Refine.

After training, you can refer to doc/Reproduce.md for reproducing our experiment result.

Performance

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. You can refer to doc/Reproduce.md for reproducing our result.

Acknowledgments