Real-Time Tennis Analytics with Multi-Modal Vision on Edge Devices
Includes videoA real-time, edge-powered tennis analytics stack combining player tracking, pose, ball trajectory, and court mapping.
Project media
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Project overview
| Area | Details |
|---|---|
| Product context | Portable, mounted-camera tennis analysis |
| Core constraint | Run the complete perception stack on an edge device |
| Vision tasks | Court keypoints, ball and player tracking, pose, and person re-identification |
I built a multi-modal vision stack for live tennis analysis without a cloud dependency. The work combined multiple perception tasks into one on-device pipeline, where latency, compute use, and consistency between modules mattered as much as standalone model quality.
My responsibilities
- Developed court keypoint detection to estimate court geometry and camera perspective.
- Built multi-frame visual models for tennis-ball and player tracking.
- Integrated human-pose features for tennis shot action recognition.
- Extracted ReID features to improve player identity consistency during tracking.
- Optimized and ran the combined pipeline on the mounted-camera edge device.
System architecture
| Module | Purpose |
|---|---|
| Court keypoint detection | Court geometry and spatial calibration |
| Ball and player detection | Multi-frame detection and tracking |
| Human pose extraction | Shot action recognition features |
| ReID feature extraction | Identity consistency during tracking |
| Edge deployment | Portable real-time inference on the mounted-camera board |
Product use
The unified output supports smart coaching, match analysis, automated highlights, and AI-assisted sports training. Keeping inference on-device reduces reliance on network availability and makes the system suitable for portable court-side operation.