Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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 annnotation for video object tracking and segmentation.
• Object-centric downstream video tasks, such as video inpainting and editing.

🚀 Updates

• 2023/04/25: We are delighted to introduce Caption-Anything ✍️, an inventive project from our lab that combines the capabilities of Segment Anything, Visual Captioning, and ChatGPT.

• 2023/04/20: We deployed [DEMO] on Hugging Face 🤗!

Demo

https://user-images.githubusercontent.com/28050374/232842703-8395af24-b13e-4b8e-aafb-e94b61e6c449.MP4

Multiple Object Tracking and Segmentation (with XMem)

https://user-images.githubusercontent.com/39208339/233035206-0a151004-6461-4deb-b782-d1dbfe691493.mp4

Video Object Tracking and Segmentation with Shot Changes (with XMem)

https://user-images.githubusercontent.com/30309970/232848349-f5e29e71-2ea4-4529-ac9a-94b9ca1e7055.mp4

Video Inpainting (with E2FGVI)

https://user-images.githubusercontent.com/28050374/232959816-07f2826f-d267-4dda-8ae5-a5132173b8f4.mp4

Get Started

Linux

# 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 --sam_model_type vit_h --port 12212

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}
}

Acknowledgements

The project is based on Segment Anything, XMem, and E2FGVI. Thanks for the authors for their efforts.