Backboard Imaging Device for Basketball Shot Analytics
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Solution Overview
Problem
Current basketball training systems lack comprehensive and real-time feedback mechanisms to improve player performance, particularly in terms of shot analysis and player tracking.
Innovation Solution
An imaging device integrated with a basketball backboard that includes a housing with a lens and a control unit equipped with processors and memory, capable of performing line detection, adjusting image data for machine-learning models, and generating data on basketball action characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an imaging device with machine-learning models is integrated into a basketball backboard to provide real-time analytics, then the quality and comprehensiveness of player performance feedback is improved, but the device complexity and cost increase
Solution Approach 1:
The patent combines multiple functions (imaging, line detection, machine learning processing, and analytics generation) into a single integrated device mounted on the basketball backboard. This merging approach improves feedback accuracy while managing system complexity by consolidating components rather than using separate systems.
Solution Approach 2:
The imaging device serves multiple functions: capturing images, detecting court lines, tracking player movements, analyzing shot characteristics, and providing real-time feedback. This multi-functionality improves the comprehensiveness of performance analytics while reducing the need for multiple separate devices.
2Measurement precision
If line detection algorithms are applied to captured image data to identify basketball court lines, then the accuracy of shot location analysis is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs line detection and court mapping in advance to establish a reference framework before actual shot analysis. By pre-identifying court lines and creating a coordinate system, the system reduces real-time processing requirements while maintaining high shot location accuracy.
Solution Approach 2:
The patent replaces complex real-time mechanical measurement systems with image processing and machine learning algorithms. The line detection uses computer vision techniques to identify court markings from captured images, substituting physical measurement tools with computational methods that can be performed more efficiently.
3Adaptability or versatility
If pixel mapping algorithms are executed to analyze basketball actions when lines are not detected, then the system can still provide analytics on non-standard courts, but the computational complexity and processing requirements increase
Solution Approach 1:
The system dynamically adjusts its analysis approach based on whether court lines are detected. When lines are present, it uses line-based coordinate mapping; when lines are absent, it switches to pixel-based action recognition. This dynamic adaptation provides versatility across different court types while managing computational complexity through conditional processing paths.
Solution Approach 2:
The patent introduces an intermediary line detection step that determines which analysis pathway to use. This intermediary process acts as a mediator between the captured image and the final analytics, selecting the appropriate algorithm based on court line presence and directing the flow to either line-based or pixel-based processing.
4Measurement precision
If multiple algorithms are applied to adjust image data for machine-learning models, then the quality of basketball action recognition is improved, but the loss of time due to multiple processing steps increases
Solution Approach 1:
The system applies image adjustment algorithms in periodic or sequential stages rather than all at once. The processing occurs in discrete steps (line detection, image adjustment, action recognition) that can be executed in a pipeline manner, improving action recognition accuracy while managing overall processing time through structured multi-stage processing.
Data Source
AI summary
An imaging device comprising a housing configured to be coupled to a basketball backboard, a lens disposed in the housing, a control unit disposed in the housing, the control unit comprising one or more processors, and a memory coupled to the one or more processors. The one or more processors are configured to perform operations stored in the memory including performing line detection from captured image data, providing, to a trained machine-learning model, adjusted image data based on one or more algorithms applied to the captured image data, wherein the one or more algorithms are based on whether or not lines are detected from the captured image data and generating, from output of the trained machine-learning model, data indicating characteristics of a basketball action associated with one or more users.