3D Object Detection for Tennis Ball Bounce Verification
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Solution Overview
Problem
Current systems for determining whether a tennis ball has bounced within or outside a game area, such as a tennis court, face challenges including high costs, complexity, inefficiency, and inaccuracies due to reliance on simulated image analysis or two-dimensional detection methods, which fail to accurately differentiate the ball from other objects and shadows.
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 a spherical element has bounced on a perimeter line or target, employing high-speed cameras and laser scanners to provide precise, real-image analysis without simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulated image analysis is used to determine ball bounce location, then detection precision can be improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses real captured images instead of simulated images to represent the ball's position and trajectory. By directly capturing and analyzing actual images of the ball during flight, the system eliminates the need for complex trajectory simulation while maintaining detection accuracy.
Solution Approach 2:
The patent extracts only the essential visual information needed for bounce determination from the captured images, focusing on key moments (ball release, bounce, landing) rather than processing complete simulated trajectories. This reduces computational complexity while preserving detection precision.
2Measurement precision
If multiple high-speed cameras are deployed to capture ball trajectory, then detection precision improves, but device complexity and cost increase
Solution Approach 1:
The patent divides the detection process into discrete key moments (ball release, bounce, landing) rather than requiring continuous trajectory capture. By focusing on these critical instants, the system can use fewer cameras strategically positioned to capture these specific moments, reducing overall system complexity.
Solution Approach 2:
The system pre-identifies and captures images at predetermined key moments in the ball's flight path. By knowing in advance what moments are critical for bounce determination, the system can optimize camera positioning and timing to capture only these essential frames, reducing the number of cameras needed.
3Productivity
If real-time image analysis is performed to determine bounce location, then productivity improves, but measurement precision may decrease due to processing time
Solution Approach 1:
The system performs preliminary capture of images at all key moments continuously, storing them for later analysis. This allows real-time detection because the critical images are already captured and ready for immediate analysis when needed, eliminating processing delays while maintaining precision.
Solution Approach 2:
The patent applies different analysis methods to different key moments based on their specific characteristics. For example, bounce detection uses specific image features and comparison methods optimized for that particular moment, improving both speed and accuracy of the analysis.
4Loss of time
If automated detection system is implemented, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system uses simple image capture and comparison techniques rather than complex automated tracking algorithms. By directly comparing captured images of the ball's position with known court boundaries and key moment templates, the system achieves rapid automated determination without requiring sophisticated complex detection hardware or software.
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 analysing the selected image to check if the spherical element has impacted or not with the component.