Track-Anything is a flexible and interactive tool for video object tracking and segmentation. It is developed upon Segment Anything, can specify anything to track and segment via user clicks only. During tracking, users can flexibly change the objects they wanna track or correct the region of interest if there are any ambiguities. These characteristics enable Track-Anything to be suitable for:
- Video object tracking and segmentation with shot changes.
- Visualized development and data annotation for video object tracking and segmentation.
- Object-centric downstream video tasks, such as video inpainting and editing.
:rocket: Updates
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2023/05/02: We uploaded tutorials in steps :world_map:. Check HERE for more details.
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2023/04/29: We improved inpainting by decoupling GPU memory usage and video length. Now Track-Anything can inpaint videos with any length! :smiley_cat: Check HERE for our GPU memory requirements.
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2023/04/25: We are delighted to introduce Caption-Anything :writing_hand:, an inventive project from our lab that combines the capabilities of Segment Anything, Visual Captioning, and ChatGPT.
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2023/04/20: We deployed DEMO on Hugging Face :hugs:!
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2023/04/14: We made Track-Anything public!
:world_map: Video Tutorials (Track-Anything Tutorials in Steps)
:joystick: Example - Multiple Object Tracking and Segmentation (with XMem)
:joystick: Example - Video Object Tracking and Segmentation with Shot Changes (with XMem)
:joystick: Example - Video Inpainting (with E2FGVI)
:computer: Get Started
Linux & Windows
# Clone the repository:
git clone https://github.com/gaomingqi/Track-Anything.git
cd Track-Anything
# Install dependencies:
pip install -r requirements.txt
# Run the Track-Anything gradio demo.
python app.py --device cuda:0
# python app.py --device cuda:0 --sam_model_type vit_b # for lower memory usage:book: Citation
If you find this work useful for your research or applications, please cite using this BibTeX:
@misc{yang2023track,
title={Track Anything: Segment Anything Meets Videos},
author={Jinyu Yang and Mingqi Gao and Zhe Li and Shang Gao and Fangjing Wang and Feng Zheng},
year={2023},
eprint={2304.11968},
archivePrefix={arXiv},
primaryClass={cs.CV}
}:clap: Acknowledgements
The project is based on Segment Anything, XMem, and E2FGVI. Thanks for the authors for their efforts.