Driver drowsiness detection through HMM based dynamic modeling

Eyosiyas Tadesse; Weihua Sheng; Meiqin Liu

doi:10.1109/ICRA.2014.6907440

Driver drowsiness detection through HMM based dynamic modeling

Eyosiyas Tadesse
, Weihua Sheng
, Meiqin Liu

Oklahoma State University
Zhejiang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

45 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4003-4008
Number of pages	6
ISBN (Electronic)	9781479936854, 9781479936854
DOIs	https://doi.org/10.1109/ICRA.2014.6907440
State	Published - 22 Sep 2014
Externally published	Yes
Event	2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: 31 May 2014 → 7 Jun 2014

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)	1050-4729

Conference

Conference	2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/Territory	China
City	Hong Kong
Period	31/05/14 → 7/06/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Drowsiness detection
HMM
SVM
facial expression

Access to Document

10.1109/ICRA.2014.6907440

Cite this

Tadesse, E., Sheng, W., & Liu, M. (2014). Driver drowsiness detection through HMM based dynamic modeling. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 4003-4008). Article 6907440 (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2014.6907440

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title = "Driver drowsiness detection through HMM based dynamic modeling",

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.",

keywords = "Drowsiness detection, HMM, SVM, facial expression",

author = "Eyosiyas Tadesse and Weihua Sheng and Meiqin Liu",

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doi = "10.1109/ICRA.2014.6907440",

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Tadesse, E, Sheng, W & Liu, M 2014, Driver drowsiness detection through HMM based dynamic modeling. in Proceedings - IEEE International Conference on Robotics and Automation., 6907440, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 4003-4008, 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 31/05/14. https://doi.org/10.1109/ICRA.2014.6907440

Driver drowsiness detection through HMM based dynamic modeling. / Tadesse, Eyosiyas; Sheng, Weihua; Liu, Meiqin.
Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4003-4008 6907440 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Driver drowsiness detection through HMM based dynamic modeling

AU - Tadesse, Eyosiyas

AU - Sheng, Weihua

AU - Liu, Meiqin

PY - 2014/9/22

Y1 - 2014/9/22

N2 - 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.

AB - 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.

KW - Drowsiness detection

KW - HMM

KW - SVM

KW - facial expression

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DO - 10.1109/ICRA.2014.6907440

M3 - 会议稿件

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BT - Proceedings - IEEE International Conference on Robotics and Automation

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014

Y2 - 31 May 2014 through 7 June 2014

ER -

Driver drowsiness detection through HMM based dynamic modeling

Abstract

Publication series

Conference

UN SDGs

Keywords

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