Basketball Goaltending Detection Using Multi-Camera Impact Tracking
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Solution Overview
Problem
Basketball referees often struggle to make quick and accurate decisions regarding goaltending infractions due to the fast-paced nature of the game, necessitating additional assistance to reduce errors.
Innovation Solution
A device that analyzes video streams from multiple cameras to determine the real-life position of a basketball and detect impacts using object detection, skeletal tracking, and impact classification, outputting signals to indicate goaltending events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If referees manually monitor and decide goaltending infractions in fast-paced basketball games, then human judgment and decision-making are maintained, but accuracy and speed of detection are insufficient
Solution Approach 1:
The patent replaces the manual mechanical system of human referees with an automated computer vision system using machine learning models and image processing algorithms to detect goaltending infractions, thereby improving both detection accuracy and reducing decision time in fast-paced basketball games
Solution Approach 2:
The patent introduces an intermediary automated analysis system that processes video feeds from multiple cameras, uses skeletal tracking to monitor player positions, and applies machine learning classification to determine goaltending infractions, serving as a mediator between the game action and referee decision-making
2Reliability
If two referees are deployed to officiate a basketball game, then coverage is improved, but human error still occurs and additional assistance is needed
Solution Approach 1:
The patent creates a universal automated system that can handle multiple functions including tracking basketball position, monitoring player skeletal movements, detecting impacts, classifying infractions, and generating reports, replacing the need for multiple human referees while improving reliability
Solution Approach 2:
The patent uses video feeds from multiple cameras to create digital copies of the game action from different angles, processes these copies through image recognition and skeletal tracking algorithms to detect infractions that may be missed by human referees
3Productivity
If automated video analysis is implemented to detect goaltending events, then detection accuracy and speed are improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex task of goaltending detection into distinct processing stages: video feed acquisition from multiple cameras, basketball detection and tracking, skeletal feature extraction from players, impact detection through motion analysis, and final classification using machine learning models, allowing each segment to be optimized independently
Solution Approach 2:
The patent performs preliminary actions by pre-processing video feeds to extract skeletal features and track basketball position continuously throughout the game, so that when a potential goaltending event occurs, the system already has the necessary data ready for rapid impact detection and classification
Data Source
AI summary
A device for detecting a goaltending event includes circuitry configured to: determine a real-life position of a basketball from a video stream; detect an impact on the basketball from the movement of the basketball captured in the video stream; output a signal indicating a detected goaltending event based on the detected impact and the real-life position of the basketball.