Mobile AI Game Tracking via Player Posture Detection
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Solution Overview
Problem
Current real-time sports analytics systems are complex and expensive, requiring multiple high-definition cameras and high-end hardware for accurate game tracking, making them inaccessible for mass adoption with low-cost, general-purpose mobile devices.
Innovation Solution
A mobile device-based system using artificial intelligence and computer vision techniques, such as convolutional neural networks, to detect player postures, track ball shots, and determine player locations in multiplayer ball games, enabling real-time analytics on a single mobile device like a smartphone or tablet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-definition cameras and high-end hardware are used for accurate game tracking, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent combines multiple camera arrays and processing systems into a unified tracking system that processes video feeds from multiple sources simultaneously. The server merges data from multiple cameras to determine player locations, shot attempts, and game events, achieving high measurement precision through data fusion rather than through individual camera complexity
Solution Approach 2:
The tracking system is designed to handle multiple game types (basketball, soccer, football, hockey) and multiple analysis functions (player tracking, shot detection, event recognition) using a single multi-functional platform. The system can process different camera configurations and adapt to various court/field layouts, reducing overall system complexity through universal design
2Measurement precision
If multiple high-definition cameras and high-end hardware are used for accurate game tracking, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The system uses standard video cameras that capture optical copies of the game scene, processing these visual copies through computer vision algorithms to extract tracking data. This approach avoids the need for expensive specialized sensors while achieving accurate player and ball location through image analysis
Solution Approach 2:
The patent replaces complex mechanical tracking systems (multiple physical cameras mounted at specific positions) with a software-based solution that uses standard cameras and AI algorithms. The mechanical complexity of precise camera positioning and synchronization is substituted with computational methods for detecting player and ball locations from video feeds
3Measurement precision
If multiple camera arrays positioned at multiple perspectives are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts to different camera configurations and game situations. The tracking algorithms adapt to varying camera positions, angles, and qualities in real-time, allowing the system to maintain measurement precision without requiring fixed, complex camera installations. The system can handle mobile devices with changing perspectives throughout the game
4Measurement precision
If massive processing power in high-end desktop and server-grade hardware is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The processing system is segmented into distributed components that can operate independently or in combination. The tracking server divides the analysis into separate modules (player detection, ball tracking, event recognition) that can be processed separately and then integrated, reducing the complexity burden on any single hardware component while maintaining overall precision
Data Source
AI summary
Methods and systems are disclosed for real-time tracking of a multiplayer ball game using a mobile computing device. The methods and systems are configured to receive an input video of the multiplayer ball game captured using a camera on the mobile computing device in a gaming area associated with n players, where n is an integer and n≥2; detect a plurality of player postures, by performing a computer vision algorithm on each of a plurality of frames of the input video; extract an associated player feature from each of the plurality of player postures; assign each of the plurality of player postures, based on the associated player feature, to one of at least n posture groups, where each player is represented by one of the at least n posture groups; and determine a player location for each player, based on the player's posture group.