Advanced Football Video Analysis Development
Budget: $10 – $50 USD
We are looking for a developer to build a football video analysis tool that can track players, detect events, and generate detailed match data.
Core Requirements:
• Player detection in match videos using YOLOv8 (or similar)
• Player tracking with Deep SORT or similar tracking algorithms
• Event recognition: passes, shots, pressures, duels, recoveries, off-ball movements
• Generation of heatmaps, movement maps, and structured match reports
• Process full match videos (90 minutes) offline
• Modular design to allow adding more statistics/features later
Preferred Tech Stack:
• Computer Vision: OpenCV, YOLOv8, DeepSORT
• Machine Learning: TensorFlow / PyTorch
• Data Handling: Python, SQL/NoSQL
• Optional: Docker for deployment, cloud GPU experience
Questions for Applicants:
• Relevant previous experience in tracking/detection/sports analytics
• Approach to handling event recognition with limited labeled data
• Ideas for structuring the system for scalability
Core Requirements:
• Player detection in match videos using YOLOv8 (or similar)
• Player tracking with Deep SORT or similar tracking algorithms
• Event recognition: passes, shots, pressures, duels, recoveries, off-ball movements
• Generation of heatmaps, movement maps, and structured match reports
• Process full match videos (90 minutes) offline
• Modular design to allow adding more statistics/features later
Preferred Tech Stack:
• Computer Vision: OpenCV, YOLOv8, DeepSORT
• Machine Learning: TensorFlow / PyTorch
• Data Handling: Python, SQL/NoSQL
• Optional: Docker for deployment, cloud GPU experience
Questions for Applicants:
• Relevant previous experience in tracking/detection/sports analytics
• Approach to handling event recognition with limited labeled data
• Ideas for structuring the system for scalability