/video-object-segmentation

video object segmentation

Primary LanguageJavaScriptMIT LicenseMIT

Video-Object-Segmentation-STCNN

Final project for EECS E6893 Big Data Analytics @ Columbia.

Introduction

In this project, we used One-Shot Video Object Segmentation method to extract foreground object and provide front-end visualization. img

Back-End Model

OSVOS is a method that tackles the task of semi-supervised video object segmentation. It is based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence.

Front-End

We also provide a web app to visualize the result. Using django to build a web interface. User can upload the original video on the web interface. When the video is uploaded, django sends request post to flask api. Then the flask api gets the video file, do data preprocess, use model to predict the segmentation label in the video(track object), rendering the video and return it back to the django. Finally, user can watch the rendered video on web interface.

Code

img

Structure

.
├── data_utils
│   ├── dataset.py # contrust data pipeline
│   └── video_utils.py # process videos
├── DAVIS # data
├── demo_tools # demo result
│   └── osvos_demo.py
├── frontend # front-end code
│   ├── app
│   ├── frontend
│   ├── upload
│   ├── manage.py
│   └── README.md
├── osvos_net # network structure
│   └── net.py
├── tmp # tmp result
├── app.py # back-end code
├── LICENSE
└── README.md

Back-End

  • The back-end api is provided by Flask. It would accept requests from front-end, process data and use model to predict. A simple cache is also provide to avoid redundant query.

  • Code related to model is inside data_utils, osvos_net and demo_tools. You can also refer to code provided by original paper.

  • Deployment

    python3 manage.py runserver

  • Temporary result will be stored in tmp.

  • Model weights will be stored in models. Notice that code related to training is split from demo part. See comments inside osvos_net for details.

Front-End

  • Use django to build a web interface. See README.md for details.

  • Deployment

    python3 app.py

Citation

OSVOS: One-Shot Video Object Segmentation

code

@Inproceedings{Cae+17,
  Title          = {One-Shot Video Object Segmentation},
  Author         = {S. Caelles and K.K. Maninis and J. Pont-Tuset and L. Leal-Taix\'e and D. Cremers and L. {Van Gool}},
  Booktitle      = {Computer Vision and Pattern Recognition (CVPR)},
  Year           = {2017}
}

DAVIS-2016 Dataset

@inproceedings{Perazzi_CVPR_2016,
  author    = {Federico Perazzi and
               Jordi Pont-Tuset and
               Brian McWilliams and
               Luc Van Gool and
               Markus Gross and
               Alexander Sorkine-Hornung},
  title     = {A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2016}
}

Presentation

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