Image Sensor Object Recognition via Individual Representations
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Solution Overview
Problem
The high cost and complexity of implementing object recognition systems due to the need for sophisticated algorithms and multiple sensors, such as optical and image sensors, RFID, and beacon technology, which increase hardware and processing requirements.
Innovation Solution
A method and apparatus that utilize an image sensor to capture images of objects, process presence and location data, and classify objects using individual representations, updating a machine learning model with classification data, which can be distributed over a network for processing and model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (optical, image, RFID, beacon) are used to capture object data, then object recognition accuracy is improved, but hardware cost and system complexity increase
Solution Approach 1:
The patent extracts and removes RFID and beacon technology from the object recognition system, relying solely on image sensors to capture both image data and presence/location data. This elimination of unnecessary components reduces hardware complexity and cost while maintaining recognition accuracy through sophisticated image processing algorithms.
Solution Approach 2:
The image sensor is designed to perform multiple functions: capturing image data for visual recognition and detecting presence/location data for spatial awareness. This multi-functional approach replaces the need for separate RFID and beacon sensors, reducing system complexity while maintaining comprehensive object detection capabilities.
2Reliability
If multiple sensors (optical, image, RFID, beacon) are deployed, then object detection capability is improved, but hardware cost increases
Solution Approach 1:
The patent replaces expensive, sophisticated sensor systems (RFID, beacon technology) with more economical image sensors. While image sensors may require more processing power, the actual hardware cost is reduced by eliminating specialized expensive components, making the system more economically viable.
3Measurement precision
If sophisticated algorithms and robust processing power are used, then object recognition accuracy is improved, but algorithm complexity increases
Solution Approach 1:
The patent segments the object recognition process into distinct stages: image capture, presence/location detection, data integration, and classification. By dividing the sophisticated algorithm into modular components, the system achieves high accuracy while making the algorithm more manageable and less complex through structured organization.
Data Source
AI summary
An image sensor is used to capture an image that includes a plurality of objects. Presence and location data is identified for the plurality of objects. The image and the presence and location data is utilized to create individual representations of the plurality of objects. The plurality of objects are classified through employment of the individual representations. A machine learning model is updated with the classification data generated by classifying the plurality of objects.