Track Anything Annotate: Video annotation and dataset generation of computer vision models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912389639700480 |
|---|---|
| author | Ivanov, Nikita Klimov, Mark Glukhikh, Dmitry Chernysheva, Tatiana Glukhikh, Igor |
| author_facet | Ivanov, Nikita Klimov, Mark Glukhikh, Dmitry Chernysheva, Tatiana Glukhikh, Igor |
| contents | Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available at https://github.com/lnikioffic/track-anything-annotate |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Track Anything Annotate: Video annotation and dataset generation of computer vision models Ivanov, Nikita Klimov, Mark Glukhikh, Dmitry Chernysheva, Tatiana Glukhikh, Igor Computer Vision and Pattern Recognition Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available at https://github.com/lnikioffic/track-anything-annotate |
| title | Track Anything Annotate: Video annotation and dataset generation of computer vision models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.17884 |