Product Recognition Using k-d Tree and Single Camera
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Solution Overview
Problem
Current retail systems face challenges such as out-of-stock situations, product misplacement, and organized retail crime, which require significant manpower and infrastructure modifications, and existing robot-based systems are inefficient due to the need for multiple cameras and resource-intensive image processing to accurately count products from images.
Innovation Solution
A computer-implemented system that uses a single camera to capture images and employs a k-d tree-based method with descriptors and a look-up table to recognize and count products, being robust to camera motion, illumination variations, and scale, without requiring additional lighting or sensors, and automates the process of retail monitoring and stock assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture images from various angles for accurate product information, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the product recognition task into distinct processing stages: image capture, descriptor extraction, k-d tree construction, and product identification. By dividing the complex task of accurate product counting from multiple cameras into modular steps, the system achieves measurement precision through systematic processing while reducing device complexity by using a single camera instead of multiple cameras.
Solution Approach 2:
The patent performs preliminary actions by pre-processing images to extract descriptors and constructing a k-d tree in advance before actual product identification. This preliminary processing of image data allows the system to achieve accurate product recognition with a single camera, eliminating the need for multiple cameras to capture images from various angles simultaneously.
2Productivity
If image processing is performed to detect and count products from captured images, then productivity is improved, but loss of time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-extracting descriptors from product images and organizing them into a k-d tree structure before actual counting operations. This pre-processing enables rapid product identification during runtime, significantly reducing the time loss associated with real-time image processing while maintaining high automation levels for productivity.
Solution Approach 2:
The patent creates descriptor copies from original product images and stores them in a k-d tree database. These descriptor copies enable rapid comparison and matching during product counting without requiring repeated processing of original high-resolution images, thereby reducing computational time and resource consumption while maintaining automation productivity.
3Device complexity
If a single camera is used to capture images, then device complexity is reduced, but measurement precision and reliability deteriorate due to camera motion and viewing angle variations
Solution Approach 1:
The patent applies parameter changes by transforming the single-camera constraint into an advantage through descriptor-based image representation. By converting images into scale-invariant descriptors and organizing them in a k-d tree, the system achieves measurement precision comparable to multiple-camera systems while using only a single camera, thereby reducing device complexity without sacrificing product recognition accuracy.
Solution Approach 2:
The patent replaces the mechanical approach of using multiple physical cameras to capture images from various angles with an information-processing approach using descriptor extraction and k-d tree matching. This substitution of mechanical complexity with computational methods enables the system to achieve reliable product recognition with a single camera, eliminating the need for complex multi-camera mechanical arrangements.
Data Source
AI summary
The system and method for recognizing and counting products within images is disclosed. The products are recognized by carrying out a nearest-neighbor search in the template feature space using a k-d tree and the product count is then obtained by using a maximum feature repeatability index for each identified feature. The system also obtains product arrangement by fitting bounding boxes around each identified product. The count of boxes thus obtained may also provide the exact number of discrete products visible in an image. A second stage of grid-based search is also carried out in the neighborhood of each detected product to detect new products that might have been missed out in the previous step. This detection is based on a confidence measure including information like histogram matching and spatial location of products. This system is also useful in verifying planogram compliance for a given product.