Camera System Target Identification Using Zip Code and Feature Data
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Solution Overview
Problem
Existing camera systems struggle to accurately distinguish targets due to unfavorable capture conditions and similarities in external appearances, leading to degraded accuracy in identifying individuals.
Innovation Solution
An information processing device that utilizes feature information extracted from camera images, combined with zip codes of capture sites, to enhance target identification by comparing registered and entered zip codes, thereby improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras are installed in fixed locations with limited freedom, then installation constraints are satisfied, but capture conditions (distance, angle, lighting) become unsuitable leading to degraded identification accuracy
Solution Approach 1:
The patent introduces a new dimension of identification by incorporating zip code information alongside facial features. This multi-dimensional approach allows accurate identification even when camera capture conditions are suboptimal, as the zip code provides additional discriminative information that compensates for poor image quality.
Solution Approach 2:
The system changes the parameters used for identification from solely visual features to a combination of visual features and location-based zip code information. This parameter expansion enables reliable identification under varied and constrained camera installation conditions.
2Reliability
If only facial feature information is used for identification, then the identification process is simple, but accuracy degrades when persons have similar facial features
Solution Approach 1:
The patent merges facial feature information with zip code information to create a composite identification key. This combination allows the system to distinguish between persons with similar facial features by adding the location-based zip code as an additional differentiating factor.
Solution Approach 2:
The identification system uses a composite information structure combining visual features and location information (zip codes). This composite approach enhances discrimination capability without requiring complex hardware, simply by integrating multiple types of information.
3Reliability
If multiple identification criteria are used to improve accuracy, then identification reliability increases, but the processing complexity and time increase
Solution Approach 1:
The system performs preliminary grouping of persons by zip code before detailed facial recognition. This pre-processing step reduces the search space and allows the system to quickly eliminate non-matching candidates, thereby maintaining fast processing speeds even with enhanced identification criteria.
Data Source
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AI summary
An information processing device (1) includes: an acquisition unit 161 that acquires feature information of a target depicted in images taken by cameras (2); a storage unit (150) that stores registration information (51) containing feature information of registered targets; and a distinction unit (162) that distinguishes, on a basis of a result of identification of the feature information acquired by the acquisition unit (161) and the feature information contained in the registration information (51), one registered target of the registered targets, the one registered target corresponding to the target in the images. The registration information (51) contains zip codes of sites relating to the registered targets. The distinction unit (162) identifies a zip code of a site relating to the target in the images and the zip codes contained in the registration information (51) with each other, and distinguishes the one registered target corresponding to the target in the images on the basis of the result of the identification of the feature information and on a basis of the identification of the zip codes.