Table Tennis Loop Drive Quality Metrics via Computer Vision
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Solution Overview
Problem
Current methods lack a comprehensive and accurate quantitative evaluation of the return quality of table tennis balls during loop drive training, failing to provide a synthetic summary of key features that impact the ball's return quality.
Innovation Solution
A system utilizing computer vision and machine learning to analyze historical and live video recordings of loop drive techniques, determining quantitative metrics such as topspin rotation, distance from the table edge, and scoring potential, and assigning weights to these metrics to generate an overall quantitative summary of the loop drive ball return quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive video analysis of loop drive techniques is performed, then measurement precision of ball return quality is improved, but device complexity increases
Solution Approach 1:
The system segments the complex video analysis task into distinct components: video recording capture, computer vision processing, machine learning model evaluation, and metric generation. Each component handles a specific aspect of the analysis, making the overall system more manageable and less complex while maintaining high measurement precision for ball return quality
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw video data and quality metrics. This intermediary layer processes video frames and extracts meaningful features (topspin rotation, distance from table edge, scoring potential) without requiring direct complex analysis of all video parameters, thereby reducing system complexity while improving measurement accuracy
2Loss of information
If multiple metrics are analyzed simultaneously, then information completeness is improved, but loss of information decreases
Solution Approach 1:
The system extracts only the most relevant metrics from video analysis: topspin rotation, distance from table edge, and scoring potential. By taking out and focusing on these specific key features rather than analyzing all possible video parameters, the system reduces information loss while keeping the analysis system manageable in complexity
Solution Approach 2:
The patent applies local quality by assigning different importance weights to different metrics based on their relevance to ball return quality. The machine learning model determines that topspin rotation is the most critical local feature, followed by distance from table edge and scoring potential, allowing the system to prioritize analysis of the most informative aspects while reducing overall complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides a comprehensive and accurate quantitative evaluation of loop drive ball return quality, enabling coaches and players to assess and improve their techniques by leveraging mature video and image processing methods, and machine learning algorithms to adjust weights based on scoring outcomes.
Implementation Method 1
Computer vision is an interdisciplinary field which grapples with how computers can be granted the ability to gain high-level understanding from digital images or videos
Implementation Method 2
Machine learning (ML) is the study of computer algorithms which automatically improve through experience. It is typically viewed as a subset of artificial intelligence (AI). Machine learning algorithms typically construct a mathematical model based on sample data
Implementation Method 3
The Magnus effect describes a phenomenon where an object travelling through a volume of gas or fluid is deflected in a manner not present when the object is not spinning, and is explained by the difference in pressure of the volume on opposite sides of the spinning object
Data Source
AI summary
Disclosed are techniques for quantifying physical qualities of a ball returned by a player using a loop drive technique, such as in table tennis, and generating a corresponding quantitative summary of the overall quality of the loop drive technique based on the quantified physical qualities. Image processing techniques are applied to historical video recordings of balls returned using loop drive techniques to quantify physical properties of said balls. A machine learning model is generated using the quantified physical properties to determine relative significance of specific qualities and their corresponding quantified values to the overall quality or success of loop drive techniques, such as in table tennis matches.