Written guidelines before volume
Class definitions, edge cases, and rejection criteria agreed and documented before bulk work begins.
Data Services
Video Annotation
Frame-by-frame labelling with persistent object identities across a sequence, for tracking and action recognition.
Overview
Video adds identity persistence to the problem. An object leaving and re-entering frame needs the same ID, and that is where most video datasets go wrong.
We handle both object tracking and action segment annotation, with review focused on identity continuity across occlusion.
What you receive
- Annotated dataset in your training format
- Written annotation guidelines
- Quality report with agreement metrics
- Sample batch for sign-off before volume
Typical stack
- CVAT
- Label Studio
- COCO
- YOLO
- Pascal VOC
- Python
Capabilities
What this covers in practice
Calibrated annotator teams
Annotators calibrated against a reference set, with agreement measured before they join a project.
Independent review pass
A second annotator reviews sampled output, with rework triggered on defined error thresholds.
Delivery in your format
COCO, YOLO, Pascal VOC, or a custom schema - exported to match your existing training pipeline.
Related
Also under Data Services
Data Engineering & Pipelines
Ingestion, transformation, and quality checks that turn scattered operational data into something a model or a report can rely on.
Read moreSemantic Segmentation
Pixel-level class labels across an image, for models where a boundary drives a measurement rather than a bounding box.
Read more2D Bounding Boxes
Rectangular object annotation for detection and counting tasks, at the volumes detection training actually needs.
Read moreWant to talk through a video annotation project?
Send us the shape of the problem and we'll come back with a scoped approach, a timeline, and an honest read on what's achievable.