Temporal precision for motion AI
Video Annotation
Frame-accurate object tracking, activity recognition, and event labeling for autonomous systems and video AI.
Overview
What is Video Annotation?
Video annotation tracks objects and events across frames with temporal consistency that static image labeling cannot provide, enabling models to understand motion, activity, and change over time.
Why it matters: Autonomous vehicles, surveillance systems, and sports analytics all depend on frame-accurate tracking. A single dropped frame or ID switch in training data can teach a model the wrong physics of motion.
Object Tracking
Persistent ID tracking across frames with occlusion handling
Activity Recognition
Action and event labeling for behavior-understanding models
Temporal Consistency
Automated validation of trajectory smoothness across sequences
Multi-Object
Dense multi-object scenes with hundreds of simultaneous tracks
Workflow
How We Do It
01
Video Segmentation
Long videos are segmented into batches for parallel processing and quality control.
02
Keyframe Annotation
Annotators label key frames with full spatial and semantic annotation per your taxonomy.
03
Temporal Tracking
AI-assisted interpolation propagates labels across frames, verified by annotators.
04
Consistency Review
Temporal QA validates object ID continuity, trajectory smoothness, and occlusion handling.
05
Delivery
Frame-level annotations delivered in your format with temporal metadata and a QA report.
Case Study
Sports Analytics Company
Sports Analytics Company
Track player and ball movement across 10,000 hours of match footage
Solution
Multi-object tracking pipeline with jersey-number ID resolution and event tagging
Results
10K
Hours tracked
96%
ID consistency
8 wks
Delivery
Ready to Get Started with Video Annotation?
Tell us about your project and we'll scope a pilot within 48 hours.