Resource-Responsive Motion Capture for Real-Time Analysis
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Solution Overview
Problem
Motion-capture systems face challenges in efficiently processing captured images due to varying computational resources, leading to uneven or unacceptable performance.
Innovation Solution
The system assesses available computational resources by benchmarking system components and adjusts image acquisition and image-analysis parameters, such as frame resolution, frame capture rate, analysis algorithm, and analysis density, to ensure optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image acquisition parameters (frame resolution, frame capture rate) and image-analysis parameters (analysis algorithm, analysis density) are set to high values to improve motion capture quality, then measurement precision and reliability are improved, but computational resource consumption increases and productivity decreases
Solution Approach 1:
The system dynamically adjusts image acquisition and analysis parameters based on real-time computational resource availability. The controller monitors resource levels and adapts frame resolution, frame capture rate, analysis algorithm complexity, and analysis density accordingly, allowing the system to optimize between quality and processing speed based on current capabilities
Solution Approach 2:
The system changes multiple parameters simultaneously to balance quality and performance: frame resolution, frame capture rate, analysis algorithm selection, and analysis density are all adjusted as a coordinated set based on benchmarked computational resources, enabling the system to match processing demands with available capacity
2Productivity
If computational resources are increased to improve real-time processing capability, then productivity is improved, but device complexity and cost increase
Solution Approach 1:
The system performs self-benchmarking to automatically assess its own computational resources and configure appropriate parameters without external intervention. The controller executes benchmarking routines and autonomously adjusts acquisition and analysis settings, eliminating the need for manual system configuration or complex setup procedures
Solution Approach 2:
The system performs benchmarking of computational resources in advance before actual motion capture operations begin. This preliminary assessment allows the system to pre-configure optimal parameters based on known resource capabilities, avoiding the need for complex real-time adjustments during operation
Data Source
AI summary
The technology disclosed relates to operating a motion-capture system responsive to available computational resources. In particular, it relates to assessing a level of image acquisition and image-analysis resources available using benchmarking of system components. In response, one or more image acquisition parameters and/or image-analysis parameters are adjusted. Acquisition and/or analysis of image data are then made compliant with the adjusted image acquisition parameters and/or image-analysis parameters. In some implementations, image acquisition parameters include frame resolution and frame capture rate and image-analysis parameters include analysis algorithm and analysis density.