Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

被引：37

作者：

Reddy Chirra V.R. ^{[1
]}

Uyyala S.R. ^{[1
,2
]}

Kishore Kolli V.K. ^{[3
]}

机构：

[1] Department of Computer Applications, National Institute of Technology, Tiruchirappalli

[2] Machine Learning and Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli

[3] Department of Computer Science and Engineering, VFSTR, Guntur

来源：

Revue d'Intelligence Artificielle | 2019年 / 33卷 / 06期

关键词：

CNN; SoftMax layer; Stacked deep convolution neural network; Viola-jones;

D O I：

10.18280/ria.330609

中图分类号：

学科分类号：

摘要：

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

引用

页码：461 / 466

页数：5

共 24 条

[1]

Amodio A., Ermidoro M., Maggi D., Formentin S., Savaresi S.M., Automatic detection of driver impairment based on pupillary light reflex, IEEE Transactions on Intelligent Transportation Systems, pp. 1-11, (2018)

[2]

Yang J.H., Mao Z.H., Tijerina L., Pilutti T., Coughlin J.F., Feron E., Detection of driver fatigue caused by sleep deprivation, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39, 4, pp. 694-705, (2009)

[3]

Hu S., Zheng G., Driver drowsiness detection with eyelid related parameters by support vector machine, Expert Systems with Applications, 36, 4, pp. 7651-7658, (2009)

[4]

Mardi Z., Ashtiani S.N., Mikaili M., EEG-based drowsiness detection for safe driving using chaotic features and statistical tests, Journal of Medical Signals and Sensors, 1, 2, pp. 130-137, (2011)

[5]

Noori S.M., Mikaeili M., Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals, J Med Signals Sens, 6, pp. 39-46, (2016)

[6]

Dang K., Sharma S., Review and comparison of face detection algorithms, 7th International Conference on Cloud Computing, Data Science & Engineering, pp. 629-633, (2017)

[7]

VenkataRamiReddy C., Kishore K.K., Bhattacharyya D., Kim T.H., Multi-feature fusion based facial expression classification using DLBP and DCT, Int J Softw Eng Appl, 8, 9, pp. 55-68, (2014)

[8]

Ramireddy C.V., Kishore K.V.K., Facial expression classification using Kernel based PCA with fused DCT and GWT features, ICCIC, Enathi, pp. 1-6, (2013)

[9]

Krajewski J., Sommer D., Trutschel U., Edwards D., Golz M., Steering wheel behavior based estimation of fatigue, The Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 118-124, (2009)

[10]

Danisman T., Bilasco I.M., Djeraba C., Ihaddadene N., Drowsy driver detection system using eye blink patterns, 2010 International Conference on Machine and Web Intelligence,, pp. 230-233, (2014)

← 1 2 3 →