Resource-Responsive Motion Capture for Variable Computing Capacity
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Solution Overview
Problem
Motion-capture systems face performance issues due to mismatches between computational resource demands and available resources, leading to uneven or unacceptable performance across different devices.
Innovation Solution
The technology adjusts image acquisition and analysis parameters based on available computational resources, tailoring the motion-capture system to optimize performance by assessing and utilizing system components effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-resolution images are captured at high frame rates, then image quality and capture speed are improved, but computational resource consumption increases
Solution Approach 1:
The system dynamically adjusts image acquisition parameters (resolution, frame rate) and analysis parameters (processing density, algorithm complexity) based on real-time assessment of available computational resources. This allows the motion capture system to optimize the balance between productivity (frame capture rate) and resource consumption (computational energy), adapting to varying system capacities without sacrificing essential performance
Solution Approach 2:
The patent changes multiple parameters simultaneously - image resolution, frame capture rate, analysis algorithm complexity, and processing density - to achieve an optimal operating point that balances image quality and capture speed against computational resource constraints. This multi-parameter adjustment resolves the contradiction by finding the right combination rather than fixing a single parameter
2Measurement precision
If complex image analysis algorithms are used to accurately detect and characterize objects, then measurement precision is improved, but processing speed decreases
Solution Approach 1:
The system applies partial action by adjusting analysis density - processing only the necessary portion of image data at full complexity, while reducing processing density in other areas. This allows accurate object detection to be achieved with reduced overall computational effort, balancing precision and speed
Solution Approach 2:
The complexity of image analysis algorithms is dynamically adjusted based on available computational resources and performance requirements. When resources are abundant, more complex algorithms provide higher precision; when resources are limited, simpler algorithms maintain acceptable precision while improving processing speed
3Device complexity
If motion capture system is standardized with fixed hardware, then device complexity is reduced, but adaptability to different computational resources worsens
Solution Approach 1:
The motion capture system achieves universality by designing software that can operate across a wide range of computational platforms with varying resources. The system assesses available resources and adapts its operation accordingly, allowing the same standardized hardware to function effectively on devices from smartphones to powerful computers, thus achieving both low device complexity and high adaptability
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.