Image Target Tracking Using Low-Resolution Region Preselection
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Solution Overview
Problem
Existing image processing systems for tracking objects require complex apparatus configurations and high processing loads, which can hinder accurate target specification and recognition.
Innovation Solution
A processing apparatus that converts high-resolution images into lower-resolution images, allowing for efficient target region specification and tracking by analyzing changes in position and features over time, reducing processing load while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are used for target tracking and recognition, then measurement precision is improved, but processing load increases
Solution Approach 1:
The image processing is segmented into two stages: first, low-resolution images are processed to identify candidate target regions; second, high-resolution images are processed only within these candidate regions for precise target specification. This segmentation reduces the overall processing load while maintaining measurement precision.
Solution Approach 2:
The system applies different image qualities to different processing needs: low-resolution images are used for broad area scanning and candidate region identification, while high-resolution images are applied only to specific candidate regions for final target recognition. This local quality differentiation optimizes both precision and processing efficiency.
2Measurement precision
If complex apparatus configuration is used for image analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing apparatus functionality is segmented between a processor that performs the core image processing operations and a storage device that stores instructions and data. This segmentation allows for a relatively simple apparatus configuration while achieving high measurement precision through sophisticated processing algorithms.
Solution Approach 2:
Low-resolution images serve as an intermediary step between full high-resolution imaging and final target recognition. This intermediary processing reduces the complexity of the apparatus by filtering out unnecessary processing of non-target regions while maintaining the ability to achieve high tracking accuracy.
3Measurement precision
If full-resolution images are processed for tracking, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The processing workflow is segmented so that speed-critical operations (candidate region identification) use low-resolution images, while precision-critical operations (target recognition) use high-resolution images only where needed. This segmentation improves overall processing speed while maintaining target position accuracy.
Solution Approach 2:
The system performs partial processing on low-resolution images to identify candidate regions, then applies full processing only to these partial regions. This partial action approach significantly improves processing speed by avoiding excessive processing of entire high-resolution images while maintaining the precision needed for accurate target position determination.
Data Source
AI summary
A processing apparatus converts a first image into a second image with a lower resolution than a resolution of the first image, specifies a target region including a predetermined objective target in the second image on the basis of the second image, and specifies a target region including the objective target in the first image on the basis of the specified target region in the second image.