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Fast and Accurate Online Video Object Segmentation via Tracking Parts (FAVOS)

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Contact: Jingchun Cheng (chengjingchun14 at 163 dot com)

Paper

Fast and Accurate Online Video Object Segmentation via Tracking Parts <br /> Jingchun Cheng, Yi-Hsuan Tsai, Wei-Chih Hung, Shengjin Wang and Ming-Hsuan Yang <br /> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (Spotlight)

This is the authors' demo (single-GPU-version) code for the DAVIS 2016 dataset as described in the above paper. Please cite our paper if you find it useful for your research.

@inproceedings{Cheng_favos_2018,
  author = {J. Cheng and Y.-H. Tsai and W.-C. Hung and S. Wang and M.-H. Yang},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title = {Fast and Accurate Online Video Object Segmentation via Tracking Parts},
  year = {2018}
}

FAVOS results

Segmentation Comparisons with Fast Online Methods

Example Video of Part Tracking

Requirements

Download DAVIS 2016 dataset, trained models, tracked parts and pre-computed results <br />

sh download_all.sh

Test our model

We provide an example testing script test_davis16.sh. <br/>

# Please run download_all.sh first
# Usage: sh test_davis16.sh <GPU-id> <sequence-name>

sh test_davis16.sh 0 blackswan

The results would be saved in results-demo/res_favos/<sequence-name>. You can replace the sequence name with others in the DAVIS 2016 validation set to obatin results for other videos. <br/>

Train your own ROISegNet

Download ResNet-101 model and save it in the folder "models" as "init.caffemodel" <br/>

cd ROISegNet
python solve.py ../models/init.caffemodel solver_davis16.prototxt 0

Tracker

We use the SiaFC tracker in Fully-Convolutional Siamese Networks for Object Tracking. <br/> The pre-computed parts and tracking results on DAVIS 2016 can be downloaded here. <br/>

Note that, we are currently working on a stable version to combine part tracking and ROISegNet for practical usage on any videos. We will update the code in a near future.

Download our segmentation results on the DAVIS datasets

Note

The models and code are available for non-commercial research purposes only.

06/2018: demo code released