Driver distraction detection with a camera vision system

Matti Kutila; Maria Jokela; Gustav Markkula; Maria Romera Rué

doi:10.1109/ICIP.2007.4379556

Driver distraction detection with a camera vision system

Matti Kutila, Maria Jokela, Gustav Markkula, Maria Romera Rué

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

84 Citations (Scopus)

Abstract

Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	VI-201-4
Number of pages	4
ISBN (Print)	978-1-4244-1436-9, 978-1-4244-1437-6
DOIs	https://doi.org/10.1109/ICIP.2007.4379556
Publication status	Published - 2007
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States Duration: 16 Sept 2007 → 19 Sept 2007

Conference

Conference	IEEE International Conference on Image Processing, ICIP 2007
Abbreviated title	ICIP 2007
Country/Territory	United States
City	San Antonio, TX
Period	16/09/07 → 19/09/07

Keywords

Machine vision
driver
distraction
SVM
camera
classification

UN SDGs

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

Access to Document

10.1109/ICIP.2007.4379556

Cite this

@inproceedings{a1a284a5b13943b4adb52ef2ecab99f4,

title = "Driver distraction detection with a camera vision system",

abstract = "Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver{\textquoteright}s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver{\textquoteright}s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.",

keywords = "Machine vision, driver, distraction, SVM, camera, classification",

author = "Matti Kutila and Maria Jokela and Gustav Markkula and {Romera Ru{\'e}}, Maria",

note = "Project code: 612; IEEE International Conference on Image Processing, ICIP 2007, ICIP 2007 ; Conference date: 16-09-2007 Through 19-09-2007",

year = "2007",

doi = "10.1109/ICIP.2007.4379556",

language = "English",

isbn = "978-1-4244-1436-9",

pages = "VI--201--4",

booktitle = "Proceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

}

Kutila, M , Jokela, M, Markkula, G & Romera Rué, M 2007, Driver distraction detection with a camera vision system. in Proceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007. IEEE Institute of Electrical and Electronic Engineers, Piscataway, NJ, USA, pp. VI-201-4, IEEE International Conference on Image Processing, ICIP 2007, San Antonio, TX, United States, 16/09/07. https://doi.org/10.1109/ICIP.2007.4379556

Driver distraction detection with a camera vision system. / Kutila, Matti ; Jokela, Maria; Markkula, Gustav et al.
Proceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007. Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers, 2007. p. VI-201-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Driver distraction detection with a camera vision system

AU - Kutila, Matti

AU - Jokela, Maria

AU - Markkula, Gustav

AU - Romera Rué, Maria

N1 - Project code: 612

PY - 2007

Y1 - 2007

N2 - Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.

AB - Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.

KW - Machine vision

KW - driver

KW - distraction

KW - SVM

KW - camera

KW - classification

U2 - 10.1109/ICIP.2007.4379556

DO - 10.1109/ICIP.2007.4379556

M3 - Conference article in proceedings

SN - 978-1-4244-1436-9

SN - 978-1-4244-1437-6

SP - VI-201-4

BT - Proceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007

PB - IEEE Institute of Electrical and Electronic Engineers

CY - Piscataway, NJ, USA

T2 - IEEE International Conference on Image Processing, ICIP 2007

Y2 - 16 September 2007 through 19 September 2007

ER -

Driver distraction detection with a camera vision system

Abstract

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this