SiamMask

This is the official inference code for SiamMask (CVPR2019). For technical details, please refer to:

Fast Online Object Tracking and Segmentation: A Unifying Approach
Qiang Wang*, Li Zhang*, Luca Bertinetto*, Weiming Hu, Philip H.S. Torr (* denotes equal contribution)
CVPR2019
[Paper] [Video] [Project Page]

Contents

1. Environment Setup
2. Demo
3. Testing Models

Environment Setup

All the code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 0.4.1, CUDA 9.2, RTX 2080 GPUs

• Clone the repository

git clone https://github.com/foolwood/SiamMask.git && cd SiamMask
export SiamMask=$PWD

• Setup python environment

conda create -n siammask python=3.6
source activate siammask
pip install -r requirements.txt
bash make.sh

• Add the project to PYTHONPATH

export PYTHONPATH=$PWD:$PYTHONPATH

Demo

• Setup your environment
• Download the SiamMask model

cd $SiamMask/experiments/siammask
wget -q http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget -q http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth

• Run demo.py

cd $SiamMask/experiments/siammask
export PYTHONPATH=$PWD:$PYTHONPATH
python ../../tools/demo.py --resume SiamMask_DAVIS.pth --config config_davis.json

Testing Models

• Setup your environment
• Download test data

cd $SiamMask/data
bash get_test_data.sh

• Download pretrained models

cd $SiamMask/experiments/siammask
wget -q http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget -q http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth

• Evaluate performance on VOT

bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2016 0
bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2018 0
python ../../tools/eval.py --dataset VOT2016 --tracker_prefix Cus  --result_dir ./test/VOT2016
python ../../tools/eval.py --dataset VOT2018 --tracker_prefix Cus  --result_dir ./test/VOT2018

• Evaluate performance on DAVIS (less than 50s)

bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth DAVIS2016 0
bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth DAVIS2017 0

• Evaluate performance on Youtube-VOS (need download data from website)

bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth ytb_vos 0

Results

These are the reproduction results from this repository. All results can be downloaded from our project page.

Tracker	VOT2016 EAO / A / R	VOT2018 EAO / A / R	DAVIS2016 J / F	DAVIS2017 J / F	Youtube-VOS J_s / J_u / F_s / F_u	Speed
SiamMask w/o Mask	0.412 / 0.623 / 0.233	0.363 / 0.584 / 0.300	- / -	- / -	- / - / - / -	76.95 FPS
SiamMask	0.433 / 0.639 / 0.214	0.380 / 0.609 / 0.276	0.713 / 0.674	0.543 / 0.585	0.602 / 0.451 / 0.582 / 0.477	56.23 FPS

Note: Speed are tested on a RTX 2080

License

Licensed under an MIT license.

Citing SiamMask

If you use this code, please cite:

@article{Wang2019SiamMask,
    title={Fast Online Object Tracking and Segmentation: A Unifying Approach},
    author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
    journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2019}
}