Multi-Model Image Processing for Real-Time Product Recognition
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Solution Overview
Problem
Existing image processing systems for product recognition are often inaccurate and impractical due to the need for external processing, leading to delays and inefficiencies in identifying objects, especially when barcodes are inaccessible or inconvenient.
Innovation Solution
A system that enables real-time product recognition on portable devices through local multi-model image processing, using multiple machine learning techniques to evaluate video frames and provide quick identification without relying on remote data communication, and includes a centralized server for distributed network access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external image processing systems are used for product recognition, then the system can provide product identification capability, but the processing time becomes impractical and accuracy deteriorates
Solution Approach 1:
The patent extracts the image processing functionality from external systems and embeds it directly into the portable device. The device now performs product recognition locally using onboard processors and machine learning models, eliminating the need to send images to external servers for processing, thus reducing processing time while maintaining accuracy
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the image capture and product identification processes. These models pre-process and analyze image data locally on the device, enabling rapid product recognition without requiring external system communication, thereby resolving the time-accuracy tradeoff
2Measurement precision
If multiple machine learning models are used for product recognition, then the accuracy improves, but the device complexity increases
Solution Approach 1:
The patent segments the product recognition task into multiple specialized machine learning models, each optimized for specific types of products or recognition scenarios. This segmentation allows the system to achieve high accuracy through model ensembles while managing complexity by organizing models into modular, independently trainable units that can be selectively deployed
Solution Approach 2:
The patent develops universal machine learning models that can handle multiple product categories and recognition tasks simultaneously. These multi-functional models reduce overall system complexity by consolidating multiple specialized functions into single versatile models, thereby improving accuracy without proportionally increasing device complexity
Data Source
AI summary
In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.