Driver drowsiness detection using ANN image processing
Open Access
- 23 October 2017
- conference paper
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 252 (1) , 012097
Abstract
The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver's eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.Keywords
This publication has 9 references indexed in Scilit:
- Driver drowsiness detection using ANN image processingIOP Conference Series: Materials Science and Engineering, 2017
- AN ANALYSIS OF ELECTRO-OCULOGRAM SIGNALS PROCESSING USING AN ARTIFICIAL NEURAL NETWORK2017
- Identification of ERD using Fuzzy Inference Systems for Brain-Computer InterfaceInternational Journal of Computers Communications & Control, 2011
- Fuzzy Algorithm for Human Drowsiness Detection DevicesStudies in Informatics and Control, 2010
- Brain–computer interfaces in neurological rehabilitationThe Lancet Neurology, 2008
- Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot studyDigital Signal Processing, 2008
- EEG feature extraction based on wavelet packet decomposition for brain computer interfaceMeasurement, 2008
- Variational Bayesian least squares: An application to brain–machine interface dataNeural Networks, 2008
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006