Ball Machine Player Tracking Neural Network
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Solution Overview
Problem
Conventional ball machines are cumbersome and inefficient in varying the type and accurate placement of ball projections for multiple players on a court, as they lack the ability to detect, track, and assign unique identifiers to individual players.
Innovation Solution
A ball machine equipped with an imaging system and neural networks to capture and analyze image data, detect persons, determine their positions, generate unique identifiers, and adjust settings for customized ball launches based on individual player positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional ball machines use manual speed control knobs for adjusting ball launch settings, then operators can customize ball projections, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The ball machine automatically detects player position and characteristics using imaging systems and neural networks, then self-adjusts launch settings without manual intervention. The system serves itself by autonomously optimizing ball projections based on real-time player data, eliminating the time-consuming manual dial-in process while maintaining customization capability
Solution Approach 2:
The patent replaces manual mechanical adjustment (speed control knobs) with automated optical detection and computational processing. Imaging systems capture player data, neural networks analyze characteristics, and computer-controlled actuators adjust launch parameters, substituting mechanical manual operation with automated electro-optical-mechanical systems
2Adaptability or versatility
If ball machines need to vary ball projection for multiple players accurately, then individualized training is achieved, but the system requires complex player detection and tracking capabilities
Solution Approach 1:
The imaging system serves multiple functions: detecting player presence, tracking position, identifying individual players, and providing data for launch optimization. This multi-functional approach consolidates what would otherwise require separate detection and tracking systems into a single integrated platform, reducing overall system complexity while enabling multi-player customization
Solution Approach 2:
Neural networks act as an intermediary layer between raw imaging data and ball launch control. The neural network processes complex image data to extract player characteristics and positions, then translates this information into actionable launch parameters, simplifying the connection between detection and actuation systems
3Measurement precision
If ball machines lack player identification capability, then the system remains simple, but accurate placement of projected balls for individual players cannot be achieved
Solution Approach 1:
The patent replaces potential complex mechanical identification systems (such as wearable tags or biometric scanners) with passive optical imaging and computational analysis. The imaging system captures visual data, and neural networks extract identifying features, eliminating the need for active identification hardware while achieving precise player differentiation
Solution Approach 2:
The system creates digital copies (image data) of players and analyzes these copies to identify and track individuals. By working with optical copies rather than requiring physical identification mechanisms, the system achieves player differentiation without adding mechanical complexity
Data Source
AI summary
A ball machine comprising an imaging system to capture image data and a processor configured to, for a frame of the image data, analyze the image data using a neural network to detect a plurality of persons, determine a coordinate position on a playing surface of each of the plurality of detected persons, extract features of each of the plurality of detected persons, generate a first set of feature vectors corresponding to the plurality of detected persons, associate a first feature vector to the coordinate position on the playing surface of a first detected person to generate a first unique identifier, associate a second feature vector to the coordinate position on the playing surface of a second detected person to generate a second unique identifier, and control the ball machine to launch balls based on first settings corresponding to the first unique identifier and second settings corresponding to the second unique identifier.