>_ PROJECT_TABLE-TENNIS_ // STATUS: COMPLETED
Table Tennis Game Analysis: Computer Vision for Sports Analytics
Computer VisionOpenCVVideo AnalysisFeature DetectionPython
> Project Overview
This project addresses the challenge of automating sports analytics for table tennis matches, which traditionally requires manual scoring and movement analysis. By leveraging computer vision techniques, I created a system that can automatically track the ball, detect player movements, and score points from standard video footage, making advanced analytics accessible without expensive specialized equipment.
> My Role
I independently developed this project from concept to implementation, applying my knowledge of computer vision and machine learning to solve a real-world sports analytics problem. This involved researching appropriate algorithms, implementing the detection and tracking systems, and creating a user-friendly interface for analysis.
> Technical Implementation
The system employs several advanced computer vision techniques to achieve reliable tracking and analysis:
- Ball Tracking: Implemented a combination of color thresholding, contour detection, and Kalman filtering to reliably track the small, fast-moving table tennis ball across frames.
- Player Movement Analysis: Used background subtraction and optical flow algorithms to isolate and track player movements, generating heatmaps of player positioning and movement patterns.
- Automated Scoring: Developed rule-based algorithms to detect when points are scored based on ball trajectory and table boundary detection.
- Performance Metrics: Calculated player statistics including movement efficiency, reaction time, and shot placement accuracy.
- Video Processing Pipeline: Created an efficient pipeline for processing video frames with OpenCV, including preprocessing for varying lighting conditions.
> Challenges & Solutions
The project presented several technical challenges that required innovative solutions:
- Ball Occlusion: Implemented predictive tracking to handle moments when the ball is temporarily obscured by players or the table.
- Variable Lighting: Developed adaptive thresholding techniques to maintain tracking accuracy across different lighting conditions.
- Real-time Processing: Optimized the code for performance to enable near real-time analysis on standard hardware.
- Distinguishing Ball from Similar Objects: Created a multi-stage verification system to differentiate the ball from other small, round objects in the frame.
> Results & Impact
The completed system successfully demonstrates the potential of computer vision for accessible sports analytics. It can process standard video recordings to provide insights that would typically require expensive specialized equipment or manual analysis. The project achieved approximately 85% accuracy in point detection and 90% accuracy in ball tracking under good lighting conditions.
> Skills Demonstrated
This project showcases several key technical competencies:
- Computer Vision Fundamentals: Object detection, tracking, and feature extraction
- OpenCV Library: Extensive use of OpenCV's image processing and analysis capabilities
- Algorithm Design: Custom algorithms for sports-specific detection and analysis
- Video Processing: Efficient handling of video streams and frame-by-frame analysis
- Problem Solving: Creative solutions to challenging computer vision problems
- Python Programming: Structured, efficient code organization for complex applications
> Future Enhancements
Potential extensions to the project include:
- Machine Learning Integration: Training a neural network to improve detection accuracy in challenging conditions
- Multi-camera Support: Combining feeds from multiple camera angles for 3D trajectory analysis
- Player Identification: Automatically identifying and tracking specific players throughout a match
- Shot Classification: Analyzing ball spin and trajectory to classify different types of shots
- Mobile Application: Developing a smartphone app version for on-the-go analysis
> Project Stats
STATUS
COMPLETED
CATEGORY
Computer Vision, OpenCV, Video Analysis