Dynamic Area-of-Interest Camera for High-Speed Sports Capture
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Solution Overview
Problem
Current camera systems in sports analysis collect data from a whole region at the same frame per second, omitting areas that have not changed or have little significance, limiting the detail and accuracy of data capture.
Innovation Solution
Utilizing a combination of lower frame rate and higher frame rate cameras to dynamically process a defined area of interest, with the latter providing higher spatial or temporal resolution in a smaller area, allowing for more detailed data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a camera collects data from a whole region at the same frame rate, then the entire scene is captured, but areas that have not changed or have little significance are included, reducing data collection efficiency
Solution Approach 1:
The camera system divides the field of view into multiple regions of interest (ROIs) and processes each region independently at different frame rates. This segmentation allows the system to capture detailed information about dynamic areas while reducing or skipping capture of static areas, thereby improving overall data collection efficiency without losing critical information.
Solution Approach 2:
Different regions of the scene are processed with different frame rates and levels of detail based on their dynamic characteristics. High-frame-rate capture is applied to regions with significant motion, while lower or no capture is applied to static regions. This local quality differentiation optimizes data collection by focusing resources where they are most needed.
2Measurement precision
If a camera uses high frame rate for the entire scene, then temporal resolution is improved, but data collection cost and processing load increase
Solution Approach 1:
The scene is divided into multiple regions, and high frame rate processing is applied only to regions where temporal resolution is critical (e.g., areas with significant motion). Other regions are processed at lower frame rates or skipped entirely. This segmentation reduces overall processing load while maintaining high temporal resolution where needed.
Solution Approach 2:
Instead of applying high frame rate capture to the entire scene (excessive action), the system applies it partially only to regions where it is necessary. This partial action approach reduces processing load and computational cost while maintaining measurement precision in critical areas.
3Measurement precision
If a camera captures the entire field of view at high resolution, then spatial detail is improved, but data collection time and bandwidth requirements increase
Solution Approach 1:
The field of view is segmented into multiple regions, and high-resolution capture is applied only to regions of interest where spatial detail is critical. Other regions are captured at lower resolution or skipped. This segmentation reduces total data collection time and bandwidth requirements while maintaining high spatial resolution where needed for analysis.
Solution Approach 2:
Different spatial resolution levels are applied to different regions based on their importance. High resolution is concentrated in regions with significant action or detail, while lower resolution is used elsewhere. This local quality approach optimizes the trade-off between spatial detail and data collection efficiency.
Data Source
AI summary
A method of data collection for cameras applicable to any sport where a greater understanding of fluid movement can be obtained by having a faster frame per second or greater resolution for certain parts of a scene. The method coupling a computer to a first stage camera and a second stage camera, focusing said first stage camera on a defined dynamic area of interest, directing said second stage camera on a reduced area of interest, replacing certain data collected from the first stage camera with data collected from the second stage camera, and outputting a compilation onto a display.