Commodity Thermal Camera AI Classification for Elevated Temperature Detection
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Solution Overview
Problem
Existing elevated temperature detection systems using commodity-based thermal cameras are ineffective due to their wide temperature variance, making them unsuitable for accurately identifying individuals with elevated temperatures.
Innovation Solution
A multi-sensor threat detection system that employs a combination of mathematics, statistics, machine learning, and computer vision to classify individuals as having normal or elevated temperatures, independent of absolute temperature measurements from the camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If commodity-based thermal cameras are used for temperature detection, then cost is reduced and accessibility is improved, but temperature measurement precision deteriorates due to wide temperature variance
Solution Approach 1:
The patent introduces an AI-based classification system as an intermediary between the thermal camera and the temperature detection task. Instead of relying on absolute temperature measurements from the commodity camera, the system uses the camera as a mediator to capture thermal images that are then processed by machine learning models to classify individuals as having normal or elevated temperatures, thereby overcoming the camera's inherent precision limitations
Solution Approach 2:
The patent transforms the detection parameter from absolute temperature values to relative temperature classification (normal vs. elevated). By changing the parameter from precise quantitative measurement to categorical classification, the system can achieve effective temperature detection using commodity cameras with wide temperature variance, as the AI model learns to identify patterns associated with elevated temperatures without requiring precise absolute measurements
2Ease of operation
If absolute temperature measurements from thermal cameras are used, then direct temperature detection is achieved, but detection accuracy deteriorates due to camera temperature variance
Solution Approach 1:
The patent replaces the mechanical/physical measurement system (thermal camera directly measuring temperature) with an information processing system (AI-based classification). Instead of relying on the physical accuracy of the thermal camera's temperature sensing mechanism, the system substitutes a machine learning model that processes thermal images and classifies temperature states, thereby achieving higher detection accuracy despite the camera's limitations
3Measurement precision
If thermal cameras with tight temperature variance are used, then temperature detection precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a computational model (AI classifier) that copies or simulates the temperature detection capability of high-precision thermal cameras. Instead of physically acquiring expensive, high-precision thermal cameras, the system uses commodity cameras combined with trained machine learning models that replicate the detection accuracy of premium equipment, thereby reducing device complexity and cost while maintaining precision
Data Source
AI summary
A multi-sensor threat detection system and method for elevated temperature detection using commodity-based thermal cameras and mask wearing compliance using optical cameras. The proposed method does not rely on the accuracy of thermal cameras, but the combination of mathematics, statistics, machine learning, artificial intelligence, computer vision and Manifold learning to construct a classifier, or set of classifiers, that are able to, either alone or working as an ensemble, evaluate a person as being ‘normal temperature’ or ‘elevated temperature’ by virtue of ‘how they present to the camera’ vs. any absolute temperature measurements from the camera itself.