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This repository contains the Pytorch implementation of our method in the paper "Deep Online Fused Video Stabilization".

Python version >= 3.6 Pytorch with CUDA >= 1.0.0 (guide is here) Install other used packages:

cd dvs
pip install -r requirements.txt --ignore-installed

Download sample video here. Uncompress the video folder under the dvs folder.

python load_frame_sensor_data.py 

Demo of curve visualization: The gyro/OIS curve visualization can be found at dvs/video/s_114_outdoor_running_trail_daytime/ControlCam_20200930_104820_real.jpg.

Note, we provide optical flow result of one test video in our Data Preparation. If you would like to generate them for all test videos, please follow FlowNet2 official website and guide below. Otherwise, you can skip this section.

Note, FlowNet2 installation is tricky. Please use Python=3.6 and Pytorch=1.0.0. More details are here or contact us for any questions.

Download FlowNet2 model FlowNet2_checkpoint.pth.tar here. Move it under folder dvs/flownet2.

python warp/read_write.py # video2frames
cd flownet2
bash install.sh # install package
bash run.sh # generate optical flow file for dataset

python inference.py
python metrics.py

The loss and metric information will be printed in the terminal. The metric numbers can be slightly different due to difference on opencv/pytorch versions.

The result is under dvs/test/stabilzation.
In s_114_outdoor_running_trail_daytime.jpg, the blue curve is the output of our models, and the green curve is the input.
s_114_outdoor_running_trail_daytime_stab.mp4 is uncropped stabilized video.
s_114_outdoor_running_trail_daytime_stab_crop.mp4 is cropped stabilized video. Note, the cropped video is generated after running the metrics code.

Download dataset for training and test here. Uncompress all.zip and move dataset_release folder under the dvs folder.

Follow FlowNet2 Preparation Section.

python warp/read_write.py --dir_path ./dataset_release # video2frames
cd flownet2
bash run_release.sh # generate optical flow file for dataset

Run training code.

python train.py

The model is saved in checkpoint/stabilzation_train.

If you use this code or dataset for your research, please cite our paper.

@inproceedings{shi2022deep,
  title={Deep Online Fused Video Stabilization},
  author={Shi, Zhenmei and Shi, Fuhao and Lai, Wei-Sheng and Liang, Chia-Kai and Liang, Yingyu},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1250--1258},
  year={2022}
}