An Automatic Driver Drowsiness Detection System using DWT and RBFNN
Ch Venkata Rami Reddy1, U. Srinivasulu Reddy2, D Mahesh Babu3
1Ch Venkata RamiReddy, Research Scholar, Department of Computer Applications, National Institute of Technology, Tiruchirappalli (T.N), India.
2U. Srinivasulu Reddy, Machine Learning & Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli (T.N), India.
3D Mahesh Babu, Department of Computer Science & Engineering, VFSTR, Guntur (A.P), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 41-44 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10080275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this work an application to recognize sleep using computer vision techniques was developed. Here an automatic approach was developed for Driver drowsiness detectionfrom low-resolution images. A method is developed to attain high accuracy with fewer training samples. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm was used. DWT was used for extracting the features from the eye region of images. Radial basis function neural network (RBFNN) was used as a classifier to detect the sleeping and non-sleeping images from the testing images. The proposed method was evaluated on our created dataset and exhibited 95.4% accuracy.
Keywords: DWT, RBFNN, Viola–Jones.
Scope of the Article: Expert Systems