Information Processing Device for Retail Customer Classification
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Solution Overview
Problem
Existing technologies face challenges in accurately classifying and tracking individuals, such as customers and salesclerks, within image data for effective analysis of customer trends in retail environments, due to insufficient classification precision and reliability.
Innovation Solution
An information processing device that analyzes image data to classify objects into specific groups, generates trace data, and calculates statistical information, using features like position, clothing, face, and behavior patterns, with the ability to update classification boundaries based on user input for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object extraction and tracking technology is used, then basic customer action recognition is achieved, but classification precision and reliability are insufficient
Solution Approach 1:
The patent segments the classification process into multiple independent components: feature extraction (position, clothing, face, behavior patterns), classification boundary determination, and statistical information calculation. This segmentation allows each component to be optimized independently, improving overall classification precision and reliability.
Solution Approach 2:
The classification boundaries are made dynamic and adjustable based on user input and statistical analysis results. The system continuously refines classification boundaries by incorporating user feedback and recalculating statistical information, enabling adaptive improvement of classification precision and reliability over time.
2Measurement precision
If multiple features (position, clothing, face, behavior) are analyzed for classification, then classification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments feature analysis into distinct modules: position feature extraction, clothing feature extraction, face feature extraction, and behavior pattern extraction. Each module processes one type of feature independently, then results are integrated for comprehensive classification. This modular approach improves classification accuracy while managing processing complexity through organized decomposition.
Solution Approach 2:
The system employs a universal classification framework that can handle multiple feature types (position, clothing, face, behavior) through a common processing architecture. The same classification boundary determination and statistical calculation mechanisms are applied across all feature types, reducing overall system complexity despite analyzing diverse features.
Data Source
AI summary
According to one embodiment, an information processing device includes a processor and a memory. The processor determines whether an object included in an image belongs to a first group or not. The processor calculates at least one of first statistical information of an object determined to belong to the first group or second statistical information of an object determined not to belong to the first group. The processor stores at least one of the first statistical or the second statistical information in the memory. The processor executes display processing for at least one of the first statistical information or the second statistical information stored in the memory.