Video labeling tool

The tool enables labeling of video data for machine learning by providing a structured workflow to create and manage frame-level bounding boxes. It targets data scientists, annotation teams, and ML engineers who need scalable, repeatable annotations for object detection, tracking, and action recognition. It supports collaboration, versioning, and standard export formats to integrate with common ML pipelines.

Conceptually, the system ingests video files, presents frames for labeling, associates each labeled object with a stable ID, and preserves per-frame geometry. It offers manual box creation, category assignment, and optional automatic tracking across adjacent frames. Outputs are provided in COCO JSON, Pascal VOC XML, or CSV to fit existing training pipelines. Core features include frame sampling, ID management, and data validation.

The tool's differentiators are its explicit support for cross-frame ID consistency, simple interpolation, and format-agnostic exports, all without requiring a separate video editor. It emphasizes data quality with validation rules that detect out-of-bounds boxes, duplicate IDs, or missing frame annotations and it records provenance for reproducibility.

Typical use cases include labeling surveillance footage, sports clips, or product videos where fast, accurate bounding boxes across frames are needed. Teams benefit from faster labeling cycles, reduced annotation drift, and direct export to standard datasets for model training and benchmarking.