TY - GEN
T1 - A visual based approach for drowsiness detection
AU - Akrout, Belhassen
AU - Mahdi, Walid
PY - 2013
Y1 - 2013
N2 - The driver drowsiness detection used in human security systems aims to decrease the number of accidents. We describe in this paper an approach developed to detect the driver drowsiness state from a video-based system. Our approach uses a noninvasive method which excludes any human related elements. The latter calculates two geometric features to calculate a non-linearly and non-stationary signal. We analyze the signal extracted from the previous step by combining the two methods EMD (Empirical Mode Decomposition) and BP (Band Power) for filtering. This analysis is confirmed by the SVM (Support Vector Machine) to classify the driver alertness state.
AB - The driver drowsiness detection used in human security systems aims to decrease the number of accidents. We describe in this paper an approach developed to detect the driver drowsiness state from a video-based system. Our approach uses a noninvasive method which excludes any human related elements. The latter calculates two geometric features to calculate a non-linearly and non-stationary signal. We analyze the signal extracted from the previous step by combining the two methods EMD (Empirical Mode Decomposition) and BP (Band Power) for filtering. This analysis is confirmed by the SVM (Support Vector Machine) to classify the driver alertness state.
UR - http://www.scopus.com/inward/record.url?scp=84892386320&partnerID=8YFLogxK
U2 - 10.1109/IVS.2013.6629650
DO - 10.1109/IVS.2013.6629650
M3 - Conference contribution
AN - SCOPUS:84892386320
SN - 9781467327558
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1324
EP - 1329
BT - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
T2 - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
Y2 - 23 June 2013 through 26 June 2013
ER -
