Objective:
Develop a robust, data-driven system to analyze and evaluate individual and team performance. The system provides actionable insights on productivity, efficiency, and overall effectiveness, enhancing decision-making and performance improvement.
Key Features:
- Camera Setup & Calibration:
- Three strategically placed cameras (center, left goal, right goal) provide comprehensive gameplay coverage.
- Calibration ensures synchronized, multi-view tracking for accurate data capture.
- Player Tracking & Pose Estimation:
- Multi-camera tracking techniques enable consistent player identification across various frames.
- Pose estimation leverages visual features like shoe color, jersey, socks, and skin tone for enhanced accuracy.
- Event Detection & Performance Analytics:
- Detection of key events such as goals, movement patterns, and player positioning.
- Real-time performance insights are generated for strategic decision-making and detailed reporting.
Results:
- Achieved synchronized camera feeds, ensuring comprehensive game coverage.
- Implemented advanced tracking algorithms for consistent player identification across views.
- Enhanced pose estimation accuracy using additional visual features.
- Delivered real-time performance analytics with minimal latency.
- Reliable detection of goals and performance metrics for improved tactical decision-making.
Customization for Sports Analysis:
- Adaptable for various sports to enhance player tracking and event detection accuracy.
- Integration with performance dashboards and analytical tools for better insights.
- Potential for real-time strategy adjustment through automated analysis.
Timeline 21 Weeks:
- Camera Setup & Calibration: 4 weeks
- Player Tracking & Pose Estimation: 8 weeks
- Event Detection & Analytics: 9 weeks
Techstack:
Python, Django Rest Framework, Linux, AWS EC2, Postgres, Docker, Deep Learning, Object Detection, YOLO
