Role
Computer Vision Engineer · UX Researcher · AI/ML Developer
Stack
YOLOv8 · MediaPipe · OpenCV · Flask · Roboflow · Python
Team
Fatima Mamu, Prachi Mehta, Valentina Arango, Yiko Li
Soccer offers young Nigerians a pathway for socioeconomic advancement, but a critical infrastructure gap exists. While talent is universal across Nigeria, access to quality coaching is not, leaving potential stars without the guidance needed to develop their skills.
The Reality: Most schools assign general PE teachers as soccer coaches due to financial constraints, creating a systematic barrier to proper tactical and technical development for 98% of young players.
U-GO is an AI-powered coaching assistant designed to democratize access to quality soccer training in Nigeria, where only 2% of young players have access to specialized coaching. The platform allows coaches to upload player videos for automated skill analysis and receive personalized training recommendations.
Research conducted across Lagos, Kaduna, and Abuja - 3 cities, 12 interviews, 5 observations
Research conducted across Lagos, Kaduna, and Abuja to understand current coaching workflows, technology adoption patterns, and critical pain points in youth soccer development.
67% of coaches used smartphones for basic tasks but were hesitant about complex apps due to limited technical training
Coaches strongly preferred visual performance indicators and simple charts over numerical scores and detailed statistics
Average training sessions lasted 90 minutes with only 10-15 minutes available for individual player analysis
Poor internet reliability required offline-capable solutions, with 40% of training locations having intermittent connectivity
Direct quotes from our research revealed the depth of the challenge:
"They love to play soccer, but really the school has not provided a situation where there is formal training for these children, especially the enthusiastic girls. There is just a common PE coach for all."
"No system to create value for soccer enthusiasts at the grassroots level. It's a socio technical issue and looking at providing soccer mentorship to everyone can really impact a lot of kids in low income communities."
Built end-to-end analysis system:
Custom trained models for player and ball detection across varied field conditions
Real-time player pose estimation and movement tracking
Field keypoint detection and perspective correction for spatial analysis
Video preprocessing, stabilization, and frame extraction
Player and ball detection and tracking on one of the test matches we recorded in Bwari, Nigeria
Field key point detection + homography transformation on the harvard vs MIT match we recorded
Usable feedback interfaces for coaches and actionable recommendations for players
Challenge:
Isolating target player performance in crowded training scenarios
Solution:
Combined YOLOv8 detection with tracking algorithms and spatial consistency checking
Achievement:
High accuracy in correctly attributing actions to target players
Conducted live testing at Harvard vs MIT soccer game and conducted testing at Bwari, Nigeria, validating tactical mapping and player tracking systems under real match conditions
Computer Vision in Uncontrolled Environments
Real-world robustness requires extensive preprocessing and adaptive thresholding. Field sports analysis benefits significantly from geometric constraints and spatial context.
User-Centered Technical Development
Research-driven architecture leads to higher adoption. Offline-first design is critical for emerging markets. Visual feedback systems are more effective than numerical metrics for non-technical users.
Cross-Functional Collaboration
Technical specifications must align with UI constraints and AI system requirements. Iterative testing with end users is essential for practical computer vision applications.
U-GO project video