Sports Analytics Computer Vision Pipeline
A sports analytics platform deployed an 8-phase computer vision pipeline that turns raw match video into structured analytics, from court detection to game awareness, with cross-cloud delivery.
The challenge
Transforming raw match video into structured analytics required human annotators. No automated pipeline existed for court detection, player tracking, ball trajectory, or game statistics. Near real-time processing was needed for coaching applications. The platform needed to scale across matches without proportional human cost.
What we built
We designed an 8-phase computer vision pipeline: Court Detection & Calibration, Ball Detection & Tracking, Player Detection, Re-ID & Tracking, Pose Estimation (2D keypoints), Basic Statistics (heatmaps, coverage), Game Awareness (rally segmentation), Advanced Statistics (points, winners/errors), and Near Real-Time Optimization. Cross-cloud architecture delivered from GCP processing to AWS S3. Semi-supervised and self-supervised training techniques reduced annotation requirements.
Outcomes
Automated annotation with no human in the loop
From court detection to advanced game statistics
GCP processing to AWS S3 delivery
Phased delivery with progressive value from Phase 1
“Raw video in, structured match analytics out. Fully automated.”