Dual Camera ROI Display Upscaling for Seamless Tracking
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Solution Overview
Problem
Existing image processing systems experience interruptions and visual unappealing transitions when tracking a region of interest (ROI) within a video stream, particularly when the object moves, due to the need for frequent updates in the field of view, leading to inefficiencies and reduced image quality.
Innovation Solution
A method involving a first image capturing device with lower resolution determining the ROI and upscaling its corners to match the higher resolution of a second image capturing device, allowing seamless tracking and updating of the ROI without breaking the video stream, using an image signal processor to register and refine the corners for smooth and efficient high-resolution image presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single high-resolution camera is used to track ROI, then image quality is improved, but bandwidth consumption and processing load increase significantly
Solution Approach 1:
The system segments the imaging task by using a low-resolution camera for broad area monitoring and ROI detection, while a high-resolution camera captures detailed images only of the identified ROI regions. This segmentation allows the high-resolution camera to transmit far fewer images, dramatically reducing bandwidth consumption while maintaining high image quality for the regions that matter most.
Solution Approach 2:
The system applies local quality enhancement by directing high-resolution imaging resources specifically to the region of interest rather than uniformly across the entire field of view. The low-resolution camera identifies ROI locations, and then the high-resolution camera focuses computational and bandwidth resources only on those specific local areas, optimizing the trade-off between image quality and resource consumption.
2Measurement precision
If ROI tracking is updated frequently to follow moving objects, then tracking accuracy is improved, but the video stream is interrupted and visual transitions become apparent
Solution Approach 1:
The low-resolution camera continuously pre-processes the video stream to detect and track moving objects in advance. When an ROI is identified, the system proactively triggers the high-resolution camera to capture images at those predetermined locations, ensuring tracking accuracy is maintained without causing interruptions or visible transitions in the overall video stream.
Solution Approach 2:
The low-resolution camera acts as an intermediary that continuously monitors the entire field of view and identifies ROI regions. This intermediary detection mechanism allows the system to update tracking information smoothly by having the low-res camera handle continuous monitoring while the high-res camera provides enhanced details only when needed, preventing video stream interruptions.
Data Source
AI summary
A method of determining a region of interest may include with a first image capturing device, determining a region of interest within a field of view common between the first image capturing device and a second image capturing device, the first image capturing device capturing an image at a lower resolution than the second image capturing device; determining a corner defining the region of interest; and upscaling the determined corner to match a corner within a field of view of an image captured by the second image capturing device.