Feature Extraction Device Reliability Assessment
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Solution Overview
Problem
Existing methods for extracting a foreground region from images in monitoring systems often result in inaccurate feature extraction due to environmental factors, leading to increased calculation and processing time, and there is a need to suppress such inaccuracies.
Innovation Solution
A feature extraction device that includes a reliability degree determination unit to assess the likelihood of a region being a recognition target, and a feature determination unit that uses weighted contributions from first and second features extracted from the region and foreground, respectively, to output the feature of the recognition target.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If background subtraction method is used to extract foreground region, then processing speed is improved, but accuracy of foreground region extraction deteriorates
Solution Approach 1:
The patent combines multiple feature extraction approaches (background subtraction method and template matching method) into a unified system. The reliability degree determination unit integrates results from both methods to assess foreground region accuracy, allowing the system to maintain high processing speed while compensating for individual method deficiencies through combined evaluation
Solution Approach 2:
The patent implements a feedback mechanism where the reliability degree determination unit evaluates the accuracy of extracted foreground regions and feeds this information back to the feature determination unit. This feedback allows dynamic adjustment of processing strategies, enabling the system to maintain high speed processing while correcting accuracy issues through iterative refinement
2Measurement precision
If complex processing methods are applied to improve foreground region extraction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the feature extraction process into distinct functional modules: a background subtraction unit, a template matching unit, and a reliability degree determination unit. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving high extraction accuracy through coordinated operation of simplified components
Solution Approach 2:
The reliability degree determination unit acts as an intermediary between the feature extraction process and the final feature determination. It evaluates the quality of extracted foreground regions and provides reliability information that guides subsequent processing, thereby managing system complexity by introducing a coordinating layer that integrates multiple extraction methods
3Measurement precision
If foreground region extraction is performed with high accuracy requirements, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by using template matching selectively - only for specific recognition targets where high accuracy is critical. For other regions, the simpler background subtraction method suffices. The reliability degree determination unit identifies which regions require enhanced processing, allowing the system to achieve high accuracy where needed without universally applying complex methods that would increase overall processing time
Data Source
AI summary
The feature extraction device according to one aspect of the present disclosure comprises: a reliability determination unit that determines a degree of reliability with respect to a second region, which is a region that has been extracted as a foreground region of an image and is within a first region that has been extracted from the image as a partial region containing a recognition subject, said degree of reliability indicating the likelihood of being the recognition subject; a feature determination unit that, on the basis of the degree of reliability, uses a first feature which is a feature extracted from the first region and a second feature which is a feature extracted from the second region to determine a feature of the recognition subject; and an output unit that outputs information indicating the determined feature of the recognition subject.