An open-source tool for creating image segmentation datasets from videos using SAM 2
Project description
ViVa-DataCreator: An Open-Source Human-in-the-Loop Data Annotation Engine for Semantic Segmentation
ViVa-DataCreator is an open-source tool for creating semantic segmentation datasets by tracking objects of interest from videos. It leverages the Segment Anything Model 2 (SAM2) and You Only Look Once (YOLO) AI models to perform segmentation and object detection, guiding users through an 8-step process to generate complete datasets ready for model training.
This tool focuses on generating semantic segmentation datasets through object tracking, utilizing SAM 2 to enhance segmentation accuracy.
Key Features
- Video-to-Dataset Conversion: Transform videos into high-quality segmentation datasets with minimal manual effort.
- SAM 2 Integration: Utilize the latest Segment Anything Model 2 for accurate and interactive segmentation.
- 8-Step Flexible Process: A comprehensive workflow that guides you through dataset creation, allowing you to move between steps as needed.
- Interactive Refinement: Manually refine segmentations for precision and quality control.
- Object Tracking Integration: Utilize YOLO and DeepSort for tracking objects of interest across video frames.
- Batch Processing: Efficiently handle large videos through configurable batch processing.
- Customizable Classes: Define and assign custom object classes with unique colors.
- Safety-Focused: Designed for safe and reliable dataset generation without hardware risks.
Documentation
For detailed usage instructions, examples, and API documentation, please refer to the ViVa-DataCreator Documentation.
Citation
If you use ViVa-DataCreator in your research, please consider adding the following citations:
ViVa-DataCreator
@software{soriano2025datacreator,
author = {Miguel Soriano-García, Diego Mercado-Ravell, Israel Becerra and Julio De La Torre-Vanegas},
title = {ViVa-DataCreator: An Open-Source Human-in-the-Loop Data Annotation Engine for Semantic Segmentation},
year = {2025},
url = {https://github.com/viva-safeland/viva_datacreator}
}
ViVa-SAFELAND Simulator
@article{soriano2025viva,
title={ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles},
author={Miguel S. Soriano-Garcia and Diego A. Mercado-Ravell},
journal={arXiv preprint arXiv:2503.14719},
year={2024}
}
Related Application
@misc{delatorre2025riskaware,
title={Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments},
author={Julio de la Torre-Vanegas and Miguel Soriano-Garcia and Israel Becerra and Diego Mercado-Ravell},
year={2025},
eprint={2505.20423},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2505.20423},
}
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