Stick Pattern Tracking via Computer Vision
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Solution Overview
Problem
Existing sports tracking systems fail to accurately capture and analyze stick techniques used by athletes during live sporting events, limiting the evaluation of athlete skill and technique.
Innovation Solution
A computer-implemented system that captures sensor data from sticks equipped with sensors, processes this data to determine stick orientations, and compares these orientations to predefined patterns to identify and evaluate stick techniques, providing detailed insights into athlete skill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialty training rooms with controlled environment devices (video, accelerometer-based systems, RFID equipment) are used to measure player shooting technique, then measurement precision of stick techniques is improved, but device complexity and loss of time increase due to confinement to single location and controlled environment requirements
Solution Approach 1:
The patent replaces complex mechanical measurement systems (accelerometers, RFID equipment, video systems) with computer vision technology using cameras and machine learning algorithms to detect and analyze stick techniques. This substitution maintains measurement precision while reducing device complexity and enabling deployment in natural game environments rather than controlled training rooms.
Solution Approach 2:
The patent introduces computer vision algorithms and machine learning models as intermediaries between the physical stick techniques and the measurement system. These intermediaries process visual data from cameras to extract stick technique information, eliminating the need for direct mechanical sensors on the equipment and reducing overall system complexity.
2Measurement precision
If specialty training rooms with controlled environment devices are used to measure player shooting technique, then measurement precision of stick techniques is improved, but loss of time increases due to confinement to single location
Solution Approach 1:
The patent creates a universal measurement system using cameras and computer vision that can function in multiple locations (training rooms, arenas, practice facilities) without requiring controlled environments. This multi-functionality eliminates time loss associated with traveling to and from specialized training facilities while maintaining measurement precision across different settings.
Solution Approach 2:
By replacing location-dependent mechanical measurement systems with computer vision technology, the patent enables measurement in diverse environments including natural game settings. This substitution removes the constraint of requiring controlled training room environments, allowing continuous measurement across multiple locations without time loss.
3Ease of operation
If basic tactical data is collected from player location and time information, then ease of operation is improved, but measurement precision of stick techniques deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with extensive stick technique data before deployment. These pre-trained models automatically classify and analyze stick techniques in real-time during games, maintaining ease of operation while achieving high measurement precision without requiring manual analysis during events.
Solution Approach 2:
The patent substitutes simple location-based tracking with computer vision-based stick technique analysis. By using cameras to capture detailed stick movements and applying machine learning algorithms to interpret this data, the system maintains ease of automated data collection while dramatically improving measurement precision of actual stick techniques used by players.
Data Source
AI summary
A system and method of tracking and classifying an athlete's stick techniques used during a sporting event are disclosed, which includes measuring detailed motions and position of at least one stick object being used in sports, producing relevant stick technique patterns and classifying those patterns in a relevant manner. Classified stick pattern data is used to improve the accuracy and clarity of sporting event metrics, the ability to quantify athlete's skill and the ability to develop athletes based on their game situation techniques. The method used allows for accurate, near real-time discovery of stick patterns during live sporting events without altering or impacting the game or an athlete's ability to perform.