Track Anything Annotate: Video annotation and dataset generation of computer vision models
This addresses the need for efficient dataset creation in computer vision, though it is incremental as it builds on existing tracking and segmentation methods.
The paper tackles the problem of time-consuming and resource-intensive labeled data preparation for machine learning by prototyping a tool for video annotation and dataset generation using tracking and segmentation, resulting in significantly accelerated dataset generation compared to manual annotation.
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