Tennis Ball Impact Detection via Segmented 3D and High-Speed Imaging
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Solution Overview
Problem
Current systems for determining whether a tennis ball has bounced within or outside a tennis court area are inefficient, often requiring expensive equipment, manual intervention, and have significant error margins, particularly in distinguishing the ball from shadows and other moving objects.
Innovation Solution
A method utilizing a three-dimensional object detection and recognition system that acquires a sequence of images, performs approximate detection, automatically selects images of the impact point, and analyzes them to determine if the ball has bounced within or outside the court or on a target, using high-speed cameras and laser scanners to achieve precise detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-speed cameras and laser scanners are used to achieve precise detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the detection task into two distinct phases: approximate detection using a three-dimensional object detection system to locate potential impact areas, and refined detection using high-speed cameras to capture detailed images of those specific areas. This segmentation allows the complex system to operate efficiently by activating expensive high-speed cameras only when and where needed, rather than continuously across the entire field.
Solution Approach 2:
The three-dimensional object detection system performs preliminary detection to identify and locate spherical elements and predict their impact points before the actual impact occurs. This preliminary action provides advance information about where to focus the high-speed cameras, enabling the system to prepare and capture images at the precise moment and location of impact, thereby improving measurement precision while reducing unnecessary camera operation.
2Reliability
If multiple detection systems are used to improve accuracy, then reliability is improved, but loss of time increases due to manual intervention
Solution Approach 1:
The system implements automatic feedback mechanisms where the three-dimensional object detection system continuously monitors the field, predicts ball trajectories, and automatically triggers the high-speed cameras when an impact is anticipated. The system also provides automatic analysis of captured images and instant communication of results to referees or players, eliminating manual image review and achieving both high reliability and rapid processing.
Solution Approach 2:
The detection system performs self-service by automatically detecting objects, predicting their behavior, selecting optimal imaging moments, capturing images, analyzing results, and communicating findings without human intervention. This automation maintains high detection reliability through multiple verification steps while minimizing time loss by eliminating manual processing bottlenecks.
3Measurement precision
If three-dimensional object detection is used to distinguish the ball from other objects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system transitions from two-dimensional image analysis to three-dimensional object detection by incorporating depth information and spatial coordinates. This dimensional enhancement allows the system to accurately distinguish spherical elements from shadows and other objects based on their three-dimensional characteristics, significantly improving object identification accuracy while the modular implementation keeps complexity manageable.
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
This approach provides high accuracy and efficiency in determining ball impacts, reducing error margins to nearly zero and eliminating the need for expensive equipment or manual intervention, while distinguishing the ball from other objects with high precision.
Implementation Method 1
acquiring a sequence of images of at least one area of surveillance of said playing field
Implementation Method 2
acquiring a sequence of images of at least one area of surveillance of said playing field covering at least part of at least said component
Data Source
AI summary
A method and system for determining whether a spherical element impacts with a component of a playing field, or arranged on or proximate thereto. The method includes acquiring images of a surveillance area of a field that covers at least part of said component, such as a delimiting perimeter line of a game area or a target, performing an approximate detection of an impact of a spherical element relative to that component or proximate thereto, with an object detection and recognition system that can discern when the detected object is indeed a spherical element, automatically selecting one of the images acquired for the same point in time and that includes the area where said impact has occurred, and analyzing the selected image to check if the spherical element has impacted or not with the component.