Computer Vision Pitch Tracking System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current baseball pitching training methods focus on improving throwing mechanics and velocity but neglect pitch location, pitch selection, and pitch movement, which are crucial for long-term success, especially at higher competitive levels.
Innovation Solution
A system utilizing a computer vision system with targets having distinct zones and vision algorithms to analyze and record pitch accuracy, velocity, and movement, providing data on pitch location, object travel path, and vertical displacement, enabling pitchers to develop and optimize their pitch selection and location skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision system with multiple cameras and processing units is implemented, then measurement precision of pitch location and movement is improved, but device complexity increases
Solution Approach 1:
The system divides the measurement task into multiple segments by using multiple cameras positioned at different locations, each capturing specific aspects of pitch movement. The computer processing unit then integrates these segmented measurements to achieve comprehensive and precise pitch location tracking without requiring a single overly complex measurement device.
Solution Approach 2:
The patent introduces computer processing units as intermediaries that receive raw data from multiple cameras, process the information through algorithms, and generate refined pitch location measurements. This intermediary processing layer enables high measurement precision while keeping individual camera components relatively simple and manageable.
2Productivity
If real-time pitch data analysis is implemented, then productivity of training feedback is improved, but use of energy by the system increases
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames and identifying key pitch movement features before comprehensive analysis. This preliminary processing reduces the computational burden required for real-time analysis, enabling efficient training feedback while minimizing energy consumption through optimized processing sequences.
Solution Approach 2:
The patent implements partial action by focusing computational resources on analyzing only the critical aspects of pitch movement that most impact training outcomes. Rather than processing every detail equally, the system selectively analyzes key parameters, achieving high training productivity with reduced energy expenditure compared to exhaustive analysis of all motion data.
Data Source
AI summary
The present application is directed to a system and method providing vision algorithms for identifying objects traveling in space and identifying the configuration of one or more targets of the objects. The system is suitably programmed to record data related to movement of objects in relation to one or more targets.