Squash Referee Decision Prediction Using Video-Based Neural Networks
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Solution Overview
Problem
Current squash refereeing methods, relying on human decisions, are prone to subjectivity and inconsistency, leading to controversies and disputes, especially in junior and college levels, due to a lack of exact measurement criteria and potential biases.
Innovation Solution
A machine learning model is developed using neural networks trained on video frames to predict squash referee decisions by extracting positional and distance values, calculating additional distance metrics, and determining the most probable decision based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human referees make decisions based on visual observation, then the decision-making process is simple and quick, but the accuracy and consistency of decisions deteriorate due to subjectivity and potential biases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the video footage and the referee decision. The model processes video frames, extracts positional data of players and ball, calculates distance metrics, and provides a recommended decision to the referee, thereby improving decision accuracy while maintaining a relatively simple implementation approach
Solution Approach 2:
The patent replaces the human visual observation and judgment mechanism with an automated machine learning system. The system uses computer vision to detect player and ball positions, applies geometric calculations for distance measurements, and uses trained neural networks to predict referee decisions, substituting mechanical and cognitive human processes with automated computational methods
2Measurement precision
If multiple video frames are analyzed with detailed position and distance calculations, then the measurement precision of player positions improves, but the loss of time for processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the relationships between video frame coordinates and real-world court positions during the training phase. The machine learning model is trained on labeled data that includes pre-computed position and distance information, allowing the system to make accurate predictions during live gameplay without performing complex calculations in real-time
Solution Approach 2:
The patent selectively extracts only the most relevant positional features from video frames rather than processing all possible data. The system focuses on key parameters such as player positions, ball position, and critical distance metrics, performing partial analysis that achieves sufficient accuracy without the computational overhead of complete frame analysis
3Reliability
If a machine learning model is implemented to improve decision consistency, then the reliability of refereeing improves, but the ease of operation deteriorates due to the complexity of model training and deployment
Solution Approach 1:
The patent implements a self-service approach where the machine learning model automatically trains itself on historical match data and continuously improves its predictions. The system self-calibrates by comparing its predictions with actual referee decisions from training data, automatically adjusting its parameters without requiring manual intervention for tuning or calibration
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model's predictions are compared with actual referee decisions. This feedback loop allows the system to learn from discrepancies and improve its accuracy over time. The model receives feedback in the form of labeled training data and adjusts its internal parameters to minimize prediction errors, thereby improving reliability while maintaining operational simplicity
Data Source
AI summary
A method for making referee decisions is provided. The method includes: extracting, by a video analyzer, a plurality of position values from a video frame of squash; calculating, by the video analyzer, a plurality of distance values between the plurality of position values; training, by the video analyzer, a neural network using input values comprising the plurality of position values and the plurality of distance values; obtaining, by the video analyzer and from the neural network, a plurality of output probability values corresponding to a plurality of decisions; and determining, by the video analyzer for a referee in a sport of squash, one of the plurality of decisions corresponding to one of the plurality of output probability values that is higher than others of the plurality of the output probability values.