Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
- Title
- Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
- Creator
- Mohan R.; Chalasani S.; Suma Christal Mary S.; Chauhan A.; Parte S.A.; Anusuya S.
- Description
- In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE.
- Source
- Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1233-1238.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Network (CNN); Extreme Learning Machine (ELM).; Regularized Extreme Learning Machine (RELM)
- Coverage
- Mohan R., Shri Ram Institute of Technology, Ece, Department, Madya Pradesh, Jabalpur, India; Chalasani S., Eluru Vatluru, Department of Computer Science and Engineering, Andhra Pradesh, India; Suma Christal Mary S., Panimalar Engineering College, Department of Information Technology, Tamilnadu, Chennai, India; Chauhan A., Deemed to Be University, Christ, Department of Life Sciences, Karnataka, Bengaluru, India; Parte S.A., Mits Gwalior, Department of Computer Science and Engineering, Madhya Pradesh, India; Anusuya S., K.Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034664-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mohan R.; Chalasani S.; Suma Christal Mary S.; Chauhan A.; Parte S.A.; Anusuya S., “Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19944.