Depth-Camera Player Performance Tracking for Fast Ball Actions
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Solution Overview
Problem
Existing systems struggle to accurately track and evaluate a player's dribbling and passing performance in sporting events due to the short duration, high speed, and frequent direction changes of the ball or puck, making it difficult to assess performance levels consistently and accurately, especially when players attempt to mask their performance intentionally or due to injury or substance influence.
Innovation Solution
A system utilizing multiple depth sensing cameras and computing devices to track and evaluate shooting, dribbling, and passing motions, incorporating machine learning to analyze video data and determine atypical performance, and employing sensors to gather additional data for comprehensive player evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tracking systems are used to monitor ball movement, then the system structure remains simple, but the tracking precision deteriorates due to the ball's high speed, short duration, and frequent direction changes
Solution Approach 1:
The system divides the tracking task into multiple specialized components: depth sensing cameras capture spatial information, machine learning models analyze dribbling patterns, and separate evaluation modules assess passing performance. This segmentation allows each component to optimize for its specific function, achieving high tracking precision without requiring a monolithic complex system
Solution Approach 2:
The patent introduces computer vision algorithms and machine learning models as intermediaries between the physical ball movement and the digital tracking data. These intermediaries process the raw camera data and translate it into meaningful performance metrics, enabling accurate tracking of high-speed, short-duration actions without directly increasing hardware complexity
2Measurement precision
If multiple depth sensing cameras and machine learning systems are deployed, then tracking precision improves, but the device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it tracks ball position, identifies dribbling actions, evaluates passing performance, and detects atypical behavior patterns. This multi-functionality allows a single core system to handle diverse evaluation tasks, reducing the need for separate specialized systems and thereby managing complexity while maintaining high evaluation accuracy
Solution Approach 2:
The system implements feedback loops where machine learning models continuously learn from tracked performance data and refine their evaluation algorithms. This feedback mechanism allows the system to improve accuracy over time without requiring proportional increases in hardware complexity, as the learning process optimizes the use of existing sensing capabilities
3Reliability
If the system monitors all player actions in detail, then performance evaluation completeness improves, but the difficulty of detecting and measuring increases due to intentional masking and environmental factors
Solution Approach 1:
The patent replaces manual performance assessment with automated machine learning-based detection systems. These systems objectively analyze camera data to identify dribbling and passing actions, eliminating the subjectivity and potential bias of human evaluation. The automated systems can detect subtle performance changes and mask manipulations that would be difficult for human observers to identify
Solution Approach 2:
The system employs depth sensing cameras that detect changes in spatial coordinates and depth information rather than relying on visual color or appearance. This approach allows the system to track the ball and player positions accurately regardless of lighting conditions, camera angles, or intentional attempts to mask performance through visual deception
4Productivity
If the system provides comprehensive real-time feedback, then player improvement potential increases, but the loss of time for data processing and analysis increases
Solution Approach 1:
The machine learning models are pre-trained on extensive datasets of dribbling and passing actions before deployment. This preliminary training allows the systems to quickly recognize and evaluate performance patterns in real-time during actual gameplay, minimizing data processing time while providing comprehensive feedback for player improvement
Data Source
AI summary
Systems and methods are provided for evaluating player performance during a sporting event. The system can use one or more sensors to capture information about actions such as shooting, dribbling, kicking and/or passing an object performed by the player and at least one processor to analyze the information in order to evaluate the player's performance. The system can determine and store at least one parameter indicative of the performance of the player in performing an action, and calculate based on the at least one stored parameter at least one value indicative of the player's performance and provide an output indicative of the assessment.