Driver Fatigue Assessment Using Multi-Modal Image Analysis
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Solution Overview
Problem
Conventional methods for assessing driver fatigue rely solely on body and facial movements, neglecting physiological conditions, resulting in inaccurate assessments.
Innovation Solution
A method using a processor connected to an image capturing device to obtain physiological, facial expression, and behavioral information from images of a driver, calculating a fatigue score through classification models trained with neural networks, and providing warnings when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use only body and facial movements for fatigue assessment, then the system complexity is low, but the measurement precision of fatigue detection is insufficient
Solution Approach 1:
The patent combines multiple assessment dimensions (physiological information from images, facial expression information, and behavioral information) into a unified fatigue assessment system. This merging of multiple data sources improves measurement precision while the integrated processing approach manages system complexity through coordinated analysis of all inputs together
2Reliability
If conventional methods rely only on image processing of body and facial movements, then the ease of operation is maintained, but the reliability of fatigue assessment deteriorates
Solution Approach 1:
The patent segments the fatigue assessment into three distinct information entries: physiological information extracted from images, facial expression information, and behavioral information. Each segment is processed independently through its own analysis pathway, then integrated to produce the final fatigue score. This segmentation improves reliability by ensuring each aspect is thoroughly analyzed while maintaining manageable complexity through modular processing
3Measurement precision
If physiological information is added to the assessment, then the measurement precision improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces complex physiological measurement devices with image-based detection. Physiological information is extracted by analyzing characteristics in captured images (such as skin color changes, pulse visibility, or other physiological indicators visible through imaging) rather than using specialized sensors. This substitution maintains measurement precision while significantly reducing the difficulty of detection and measurement infrastructure
Data Source
AI summary
A method for assessing driver fatigue is implemented by a processor and includes steps of: based on images of a driver captured by an image capturing device, obtaining an entry of physiological information that indicates a physiological state of the driver; based on one of the images of the driver, obtaining an entry of facial expression information that indicates an emotional state of the driver; based on one of the images of the driver, obtaining an entry of behavioral information that indicates driver behavior of the driver; and based on the entry of physiological information, the entry of facial expression information and the entry of behavioral information, obtaining a fatigue score that indicates a level of fatigue of the driver.