Development of an AI-Powered Computer Vision System for Sports Training Analytics

Current manual tracking of training progress and game performance in sports academies is resource-intensive and limits data analysis capabilities. The client faces difficulties in scaling data collection, providing accurate individual metrics, and engaging parents or stakeholders effectively due to lack of automated analytics tools.

A mid-sized sports training academy or educational institution specializing in youth athletic development, seeking to automate performance analysis and enhance engagement with stakeholders.

• Implement an automated system to collect and process diverse sports performance data efficiently.
• Improve accuracy and granularity of individual player metrics to inform tailored training programs.
• Reduce manual effort and resource utilization during training and match analysis.
• Enhance stakeholder engagement by providing precise, real-time statistical insights to players' parents and coaches.
• Integrate both optical and wearable data sources to deliver comprehensive analytics without expensive equipment.

• Camera calibration and ground marking setup for accurate spatial data collection.
• Real-time detection and tracking of players and objects using neural network-based object detection algorithms.
• Identification of individual players through unique visual or behavioral reference features.
• Action recognition capabilities including dribbling, passing, intercepting, jumping, and tackling.
• Data output in the form of coordinates, action types, and performance statistics for analysis.
• Generation of automatic performance reports and visualizations for coaches and stakeholders.

• Video input sources from cameras and surveillance infrastructure
• Data storage solutions for large volumes of visual and behavioral data
• Reporting and visualization dashboards for stakeholders
• Potential integration with existing sports management platforms

• System must process multiple venues simultaneously, supporting over 150 grounds with varying sizes and lighting conditions.
• Ensure high accuracy in detection and tracking, approaching the precision levels of expensive optical systems.
• System should operate in real-time or near-real-time to facilitate immediate feedback.
• Scalable architecture to accommodate data growth as more training sessions and matches are recorded.
• Data security and privacy protocols to protect player and stakeholder information.

The AI-powered analytics platform is expected to automate data collection, significantly reduce manual efforts, and provide highly accurate performance metrics. This will enable coaches to develop personalized training programs, while stakeholders—such as parents—gain access to detailed progress reports. The system aims to process data across multiple grounds efficiently, supporting large-scale deployment and improving overall training effectiveness and stakeholder engagement.