TY - GEN
T1 - Driver Drowsiness Detection System Based on Visual Features
AU - Fouzia,
AU - Roopalakshmi, R.
AU - Rathod, Jayantkumar A.
AU - Shetty, Ashwitha S.
AU - Supriya, K.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/26
Y1 - 2018/9/26
N2 - Nowadays, Driver drowsiness is one of the maj or cause for most of the accidents in the world. Detecting the driver eye tiredness is the easiest way for measuring the drowsiness of driver. The existing systems in the literature, are providing slightly less accurate results due to low clarity in images and videos, which may result due to variations in the camera positions. In order to solve this problem, a driver drowsiness detection system is proposed in this paper, which makes use of eye blink counts for detecting the drowsiness. Specifically, the proposed framework, continuously analyzes the eye movement of the driver and alerts the driver by activating the vibrator when he/she is drowsy. When the eyes are detected closed for too long time, a vibrator signal is generated to warn the driver. The experimental results of the proposed system, which is implemented on Open CV and Raspberry Pi environment with a single camera view, illustrate the good performance of the system in terms of accurate drowsiness detection results and thereby reduces the road accidents.
AB - Nowadays, Driver drowsiness is one of the maj or cause for most of the accidents in the world. Detecting the driver eye tiredness is the easiest way for measuring the drowsiness of driver. The existing systems in the literature, are providing slightly less accurate results due to low clarity in images and videos, which may result due to variations in the camera positions. In order to solve this problem, a driver drowsiness detection system is proposed in this paper, which makes use of eye blink counts for detecting the drowsiness. Specifically, the proposed framework, continuously analyzes the eye movement of the driver and alerts the driver by activating the vibrator when he/she is drowsy. When the eyes are detected closed for too long time, a vibrator signal is generated to warn the driver. The experimental results of the proposed system, which is implemented on Open CV and Raspberry Pi environment with a single camera view, illustrate the good performance of the system in terms of accurate drowsiness detection results and thereby reduces the road accidents.
UR - https://www.scopus.com/pages/publications/85059872232
UR - https://www.scopus.com/pages/publications/85059872232#tab=citedBy
U2 - 10.1109/ICICCT.2018.8473203
DO - 10.1109/ICICCT.2018.8473203
M3 - Conference contribution
AN - SCOPUS:85059872232
T3 - Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018
SP - 1344
EP - 1347
BT - Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Inventive Communication and Computational Technologies, ICICCT 2018
Y2 - 20 April 2018 through 21 April 2018
ER -