The challenge
A golf swing is a fast, multi-stage movement. Useful coaching depends on understanding how the golfer’s body and equipment move over time—not just what appears in one frame.
The system needed to turn ordinary user-recorded video into information that could support actionable feedback.
- Identify the golfer and relevant features across frames
- Detect important swing phases
- Estimate body pose and movement
- Compare posture and technique
- Turn structured analysis into understandable feedback
- Expose the pipeline through a production API
What was built
The backend processes uploaded swing videos, extracts pose landmarks, identifies swing stages, uses visual detection for equipment and scene information, converts motion into technique information, and transforms structured analysis into feedback.
- Swing video processing
- Pose estimation
- Swing-event detection
- Club and ball tracking
- Technique and posture analysis
- Personalized AI feedback
- Production API and cloud deployment
System architecture
The important engineering challenge was not any one component. It was making the components work together as a usable product pipeline.
Tools and measures
- MediaPipe
- YOLOv8
- OpenCV
- FastAPI
- Docker
- Google Cloud
Scope note
The video-analysis pipeline was exposed through a backend API, containerized, deployed on Google Cloud, and integrated with the Golf Vision AI iOS product.
Reliability in a video-analysis product
Consumer video is unpredictable. Users record from different angles, lighting conditions, backgrounds, devices, distances, and swing styles.
A dependable product needs to determine whether the golfer is visible, landmarks are consistent, stages can be identified, camera position supports the requested measurement, and visual evidence is strong enough for feedback.
From computer vision to a user-facing product
Rather than returning detections or pose coordinates, the system converts visual signals into structured swing analysis and understandable coaching feedback across computer vision, temporal analysis, backend engineering, API design, deployment, and application integration.
Results & scope
The system was developed as the AI/video-analysis backend for Golf Vision AI and integrated into its iOS product workflow.
Its value as a case study is the complete engineering of a computer-vision product—from raw consumer video through analysis and AI feedback to a deployed application backend.