Single-Camera Machine Learning for Strike-Zone Position Detection
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Solution Overview
Problem
Conventional automated officiating systems for baseball and softball games require multiple cameras and complex calibration processes, making them impractical for amateur games, and there is a shortage of human umpires.
Innovation Solution
A machine learning model trained using a single, off-the-shelf camera to detect the position of a moving object relative to a reference object, such as a baseball to a strike zone, without precise positioning requirements, utilizing 2D and 3D data for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and sophisticated equipment are used for automated officiating, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential function of automated officiating from a complex multi-camera system and implements it using a single camera with machine learning. The system takes out only the necessary computational elements (neural network model) from the complex hardware setup, achieving accurate ball and strike calls without requiring multiple cameras or sophisticated equipment.
Solution Approach 2:
The patent replaces the mechanical/optical system of multiple cameras with a computational system using a single camera and machine learning model. Instead of using complex hardware configurations to achieve precision, the system substitutes mechanical complexity with intelligent software processing that can accurately determine whether pitches are balls or strikes.
2Measurement precision
If multiple cameras and complex calibration processes are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The machine learning model performs self-calibration by learning to identify the strike zone and pitch characteristics automatically during the inference process. The system serves itself by adapting to different game conditions and camera positions without requiring manual calibration procedures, making the system easy to operate while maintaining measurement precision.
3Productivity
If conventional automated systems are deployed, then productivity is improved, but device complexity increases
Solution Approach 1:
The single camera system is designed to be universal and multi-functional, serving as both the capture device and the reference frame for automated officiating. The machine learning model can process videos from various camera positions and configurations, making the system adaptable to different amateur game settings without requiring specialized equipment, thereby improving productivity through widespread deployability.
Data Source
AI summary
A system is disclosed for training and inference (implementation) of a machine learning model (“MLM”) for automated determination of a position of a moving object relative to a reference object. Such a system may for example be trained and used to call balls and strikes in baseball and softball games. The training and inference of the MLM may be accomplished using a single, off-the-shelf camera, such as those incorporated in iPhones, Androids, Google and other mobile phones.