High-Quality Video Annotation Services That Turn Raw Footage Into Model-Ready Data

Assigning accurate category and content labels to entire video clips makes your AI model reliable for content type, scene, and context recognition.

Frame-by-frame object detection and tracking annotation that maintains a consistent object identity across the full length of a clip, even through occlusion and re-entry.

Labelling human and object actions and activities as they unfold across a sequence of frames, built for behaviour recognition and activity classification models.

Identifying and labelling key events and scene changes within footage, so your model learns to recognise what is happening.

Dividing video into precisely timed segments based on action, event, or scene boundaries gives your AI model clean, correctly bounded training intervals.

Identifying and annotating the frames that matter most within a sequence, reducing redundant labelling while preserving the data your model actually needs.

Frame accurate 2D bounding boxes drawn around objects of interest, maintained consistently across every frame an object appears in.

Pixel-level polygon and semantic segmentation annotation for objects with irregular shapes, where a bounding box alone would not capture enough detail.

Keypoint and pose annotation across human bodies, faces, or object joints, tracked frame by frame for AI models built on movement and posture data.

Tagging specific attributes of an object or person within a frame, such as colour, size, orientation, or condition, adds depth to your object detection annotation.

Precise timestamp tagging for specific relevant events within a video, built for AI models that depend on exact timing as much as accurate labelling.

Writing objective, descriptive captions for video content, structured for AI models that need to be trained on vision and language tasks.

Synchronised annotation across audio and visual tracks for models that need to learn from sound and image data together.