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Demo code of the paper "Fast and Accurate Online Video Object Segmentation via Tracking Parts", in CVPR 2018

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

![Alt Text](https://github.com/JingchunCheng/FAVOS/blob/master/figures/framework.png)

Contact: Jingchun Cheng (chengjingchun14 at 163 dot com)

## Paper
[Fast and Accurate Online Video Object Segmentation via Tracking Parts](https://arxiv.org/abs/1806.02323) 
[Jingchun Cheng](https://sites.google.com/view/jingchun-cheng), [Yi-Hsuan Tsai](https://sites.google.com/site/yihsuantsai/home), [Wei-Chih Hung](https://hfslyc.github.io/), [Shengjin Wang](http://www.tsinghua.edu.cn/publish/eeen/3784/2010/20101219115601212198627/20101219115601212198627_.html) and [Ming-Hsuan Yang](http://faculty.ucmerced.edu/mhyang/) 
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](https://www.dropbox.com/s/l95ozepuohie7x4/DAVIS16_segmentation_comparison_methods_with_strong_applicability.avi?dl=0)

[Example Video of Part Tracking](https://www.dropbox.com/s/3yszhdjz6klpmzr/Illustration_part_tracking.avi?dl=0)

## Requirements
* caffe (pycaffe)
* opencv
* matlab
* A GPU with at least 12GB memory

Download DAVIS 2016 dataset, trained models, tracked parts and pre-computed results 
```
sh download_all.sh
```

## Test our model
We provide an example testing script `test_davis16.sh`. 
```
# 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.

## Train your own ROISegNet
Download [ResNet-101 model](https://www.dropbox.com/s/2506oyjkwy7acjv/init.caffemodel?dl=0) and save it in the folder "models" as "init.caffemodel" 
```
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](https://github.com/bertinetto/siamese-fc). 
The pre-computed parts and tracking results on DAVIS 2016 can be downloaded [here](https://www.dropbox.com/s/pkqlzlhwun4qwuu/parts_DAVIS2016.tar?dl=0).

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
* FAVOS on DAVIS2016 [link](https://www.dropbox.com/s/9zwob31bz91u75h/favos.tar?dl=0)
* FAVOS on DAVIS2017 [link](https://www.dropbox.com/s/8gtcgf27qdhzyqu/favos_2017.tar?dl=0)

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

06/2018: demo code released

FAVOS

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