CNN Ball Carrier Detection via Player Formation Analysis
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Solution Overview
Problem
Conventional ball carrier tracking in team sports, such as American football and rugby, is inadequate due to the difficulty in visually identifying the ball amidst the action on the field, leading to inaccurate manual tracking and inaccurate 2D sensor-based solutions that lack height information and require complex calibration.
Innovation Solution
A convolutional neural network (CNN) based algorithm treats each player as a pixel in a grid, encoding player characteristics like position, velocity, and orientation to automatically detect and track the ball carrier, providing high accuracy by learning player formations and movement patterns from bird's eye view images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition and tracking applications are used to track the ball, then the system is simple to implement, but the ball carrier detection accuracy is insufficient because the ball is often too difficult to see during action on the field
Solution Approach 1:
The patent introduces an intermediary computational model (CNN-based ball carrier estimation system) that processes player position data to indirectly determine ball carrier identity. Instead of directly tracking the difficult-to-see ball, the system uses player formation patterns, movement vectors, and spatial relationships as intermediate indicators to infer ball carrier location, thereby achieving high detection accuracy without requiring complex ball tracking hardware
Solution Approach 2:
The patent replaces traditional mechanical/optical ball tracking systems with a computational approach using convolutional neural networks. The system substitutes physical ball tracking mechanisms with algorithmic analysis of player positioning data, transforming the problem from direct visual ball detection to pattern recognition in player spatial relationships, which achieves superior accuracy without additional hardware complexity
2Productivity
If manual tracking methods are used to identify the ball carrier, then the system complexity is low, but the productivity and accuracy are insufficient due to the difficulty of visually identifying the ball amidst action
Solution Approach 1:
The patent implements a self-service system where the computational model automatically processes player tracking data and identifies ball carriers without human intervention. The CNN-based system continuously analyzes player positions, movements, and formations in real-time, autonomously determining ball carrier identity and providing results instantaneously, thereby achieving high productivity while maintaining relatively simple system architecture
Solution Approach 2:
The patent replaces manual visual tracking with automated computational analysis. Instead of human operators visually searching for the ball among players, the system uses algorithmic processing of player position data to automatically identify ball carriers, dramatically increasing identification speed while keeping system complexity manageable through software-based solutions
3Measurement precision
If 2D sensor-based solutions are used for ball tracking, then the device complexity is reduced, but the measurement precision is insufficient due to lack of height information and requiring complex calibration
Solution Approach 1:
The patent uses player position data as an intermediary to infer ball carrier information without requiring direct ball tracking. By analyzing the spatial relationships and movements of players relative to each other, the system indirectly determines ball carrier location and height information, achieving high measurement precision while avoiding the need for complex 3D sensor calibration
Solution Approach 2:
The patent replaces physical 3D sensing and calibration systems with computational geometry algorithms. Instead of using complex 3D sensors that require calibration to obtain height information, the system uses 2D player position data combined with movement analysis and spatial reasoning to calculate ball carrier characteristics, achieving accurate 3D information without physical 3D sensing hardware
Data Source
AI summary
A method and system of automatically estimating a ball carrier in team sports.