NDLI: Driver drowsiness detection through HMM based dynamic modeling

Content Provider	IEEE Xplore Digital Library
Author	Tadesse, E. Weihua Sheng Meiqin Liu
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Tadesse, E.; Weihua Sheng) \|\| Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China (Meiqin Liu)
Abstract	Drowsiness is one of the main causes of severe traffic accidents occurring in our daily life. In order to reduce the number of drowsiness-induced accidents, various researches have been conducted with the aim of finding practical and non-invasive drowsiness detection systems by using behavioral measuring techniques. Many of the previous works on behavioral measuring techniques have mainly focused on the analysis of eye closure and blinking of the driver. It is recently that more attention started to shift to inclusion of other facial expressions and only few, among those researches, have been done on the analysis of temporal dynamics of facial expressions for drowsiness detection. In this paper we propose a new method of analyzing the facial expression of the driver through Hidden Markov Model (HMM) based dynamic modeling to detect drowsiness. We have implemented the algorithm using a simulated driving setup. Experimental results verified the effectiveness of the proposed method.
Starting Page	4003
Ending Page	4008
File Size	981938
Page Count	6
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6907440
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Vehicles Feature extraction Accuracy Support vector machines Face Heuristic algorithms HMM Drowsiness detection facial expression SVM
Content Type	Text
Resource Type	Article

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