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ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Computer Vision
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1437084
An Effective Approach for Real-time Drowsy Driving Prediction Using Quantized Fisher-Gabor Features and Latent-Dynamic Conditional Random Fields
Provisionally accepted- 1Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- 2Department of Computer Science and Engineering, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, India
- 3College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
- 4Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
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Driver drowsiness or fatigue is among the most important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver drowsiness or fatigue. In this paper, an automated vision-based system for real-time prediction of driver drowsiness or fatigue is presented, in which multiple visual ocular features such as eye closure, eyebrow shape, eye blinking, and other perfectly defined geometric facial features are employed as robust cues for driver's drowsiness. In addition, an efficient scheme is applied to extract local Gabor features of driver images based on Fisher's quantum information.A novel Fisher-Gabor descriptor (FGD) is then constructed from the extracted features, which is invariant to scale and rotation and also robust to changes in illumination, noise, and minor changes in viewpoint. The normalized FGDs are ultimately fed to a Latent Dynamic Conditional Random Field (LDCRF) classification model to predict whether the driver is drowsy/fatigued and a warning signal is thus issued (if required). A series of intensive experiments conducted on the benchmark NTHU-DDD video dataset show that the proposed system can predict driver drowsiness or fatigue effectively and efficiently, exceeding several state-of-the art alternatives by achieving a competitive detection accuracy
Keywords: Drowsy driving prediction, Fisher-Gabor facial features, LDCRF classification, NTHU-DDD dataset, Intelligent 5 Transportation Systems
Received: 23 May 2024; Accepted: 18 Mar 2025.
Copyright: © 2025 Bakheet, Al-Hamadi and Alanazi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Samy Bakheet, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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