Multi-Camera Basketball Box Scoring From Arbitrary Video Angles
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Solution Overview
Problem
Existing basketball game analysis systems face challenges in accurately analyzing videos captured from arbitrary angles due to camera movements and player occlusions, leading to information loss and errors, especially in multiplayer scenarios, and existing AI technologies are prone to errors in manual labeling.
Innovation Solution
An automatic real-time basketball box score recording and analysis system that utilizes multiple cameras to capture videos from arbitrary angles, integrating a computing device for shot and player analysis, including shot type determination, posture evaluation, and automatic score recording, with an interface device for data modification and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single view camera is used for basketball game analysis, then the device complexity is reduced, but the measurement precision and information completeness deteriorate due to severe information loss from limited camera view and player occlusions
Solution Approach 1:
The system segments the basketball court coverage into multiple camera views (e.g., court level, baseline, sideline cameras) where each camera captures specific zones. This segmentation allows comprehensive coverage of shot events from multiple angles simultaneously, resolving the information loss problem of single-view cameras while maintaining manageable system complexity through modular camera placement.
Solution Approach 2:
The system transitions from two-dimensional planar court views to three-dimensional spatial analysis by incorporating cameras at different heights and angles (court level, baseline, sideline). This multi-dimensional perspective enables accurate tracking of ball trajectory, player positions, and shot status even during occlusions, significantly improving measurement precision without requiring an overly complex camera network.
2Measurement precision
If wearable sensors are disposed on players and ball to achieve game analysis, then the measurement precision improves, but the device complexity and cost increase due to additional expensive sensor-receivers
Solution Approach 1:
The system replaces mechanical wearable sensors with optical vision-based detection using multiple cameras. Computer vision algorithms track player positions, ball trajectory, and shot events by analyzing video frames from various camera angles. This substitution eliminates the need for expensive wearable sensors and receivers while achieving comparable or superior measurement precision through non-contact optical methods.
Solution Approach 2:
Instead of using physical sensors attached to players and ball, the system creates digital copies (video images) of players and ball from multiple camera perspectives. These visual copies are processed through computer vision to extract positional and motion information, providing an alternative measurement approach that reduces hardware complexity and cost while maintaining tracking precision.
3Device complexity
If manual manner is used for basketball data recording, then the device complexity is reduced, but the productivity and accuracy deteriorate due to errors and omissions
Solution Approach 1:
The system implements self-service automated data recording where computer vision algorithms automatically detect shot events, determine shot types (two-point, three-point, free throw), and calculate scores without human intervention. The system processes video frames in real-time to generate box score data, eliminating manual recording errors and significantly improving data recording efficiency and productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where detected shot events and calculated scores are continuously verified and refined through multi-camera cross-validation. The automated system provides real-time feedback on shot status determination, allowing for immediate correction of potential errors and ensuring high accuracy in data recording without requiring manual review.
Data Source
AI summary
An automatic real-time basketball box score recording and analysis system with videos captured from arbitrary angles is applied to a basketball game. A shot analyzing step is performed to analyze the videos to determine whether one of the videos is a real shot event to generate a shot analysis result. A data calculating step is performed to analyze the videos to determine that the real shot event is a three-point shot, a two-point shot or a free throw to generate a scoring result, and then calculate a game data according to the shot analysis result and the scoring result. A player analyzing step is performed to analyze the videos to obtain a posture analysis data of a shooter. The posture analysis data is configured to evaluate a performance of the shooter when shooting. An interface device is configured to modify and display the game data.