Cookware Detection and Tracking Using Optical and Thermal Cameras
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Solution Overview
Problem
Current systems lack the ability to effectively detect and track cookware items on a cooking surface, which hinders automated cooking processes and user feedback.
Innovation Solution
A system utilizing integrated optical and thermal cameras, combined with machine-learning algorithms, to capture and process images of a cooking surface, identify, track, and associate cookware items, providing real-time feedback and automated recipe generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and machine-learning algorithms are integrated to detect and track cookware items, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides the detection task into multiple specialized components: an optical camera captures visual information about cookware items, a thermal camera captures temperature information, and machine-learning algorithms process these separate data streams. This segmentation allows each component to optimize for its specific function while collectively achieving high detection precision across multiple parameters.
Solution Approach 2:
The patent combines multiple camera systems (optical and thermal) and integrates them with machine-learning processing to create a unified detection system. By merging these different sensing modalities and processing approaches, the system achieves comprehensive cookware detection and tracking with high precision across visual and thermal properties.
2Productivity
If real-time detection and tracking of cookware items is implemented, then productivity and cooking efficiency are improved, but use of energy increases
Solution Approach 1:
The system performs detection and tracking at periodic intervals rather than continuously, capturing images at specific time points during the cooking process. This periodic operation maintains real-time monitoring capability for productivity improvement while reducing overall energy consumption compared to continuous operation.
Solution Approach 2:
The machine-learning algorithms automatically process the captured images to identify and track cookware items without requiring manual intervention. The system self-manages the detection, tracking, and data processing functions, improving cooking efficiency through automation while optimizing energy usage through efficient algorithmic processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate detection and tracking of cookware items, improving cooking processes by providing users with real-time feedback and automated recipe guidance, enhancing cooking efficiency and safety.
Implementation Method 1
Cameras can include optical sensors, such as a camera that senses radiation in the visible color spectrum
Implementation Method 2
Cameras can also include thermal cameras, which typically detect radiation in the infrared spectrum, which can be used to determine the temperature of objects within the field of view of the thermal camera
Data Source
AI summary
In one embodiment, a method includes accessing an image of a cooking surface, where the image is associated with a field of view that includes the cooking surface. The method further includes identifying, based at least on the image, one or more cookware items within the field of view, associating a unique identifier with each identified cookware item in the field of view, and tracking, using one or more subsequent images of the cooking surface, a location of each of the one or more cookware items relative to the cooking surface.