Soccer Performance Analytics: Leveraging YOLOv11 and Image Processing for Data-Driven Insights
DOI:
https://doi.org/10.52783/jns.v14.1795Keywords:
Soccer Analytics, YOLOv11, Object detection, Player Tracking, Ball Detection, Machine Learning, K-means clustering, Optical flow, AI reporting, Image Processing, Perspective Transformation.Abstract
In the rapidly evolving field of soccer analytics, leveraging advanced technologies is essential for performance evaluation and strategic decision-making. This project utilizes the newly introduced YOLOv11, a state-of-the-art object detection model, to detect and track players and the ball in video footage from multiple camera angles. Integrated object trackers ensure continuity across frames, while K-means clustering classifies players into teams based on t-shirt colors. Through image processing techniques like optical flow and perspective transformation, player movements are converted into real-world measurements, enabling precise assessments of speed and distance covered. Additionally, Gemini AI is incorporated to automatically generate detailed reports based on the analyzed data, providing comprehensive insights for coaches, analysts, and sports authorities. This system aims to empower data-driven decision-making in soccer management and elevate the effectiveness of performance analysis.
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