Driver distraction detection with a camera vision system

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

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    83 Citations (Scopus)


    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 languageEnglish
    Title of host publicationProceedings of the 2007 IEEE International Conference on Image Processing, ICIP 2007
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages4
    ISBN (Print)978-1-4244-1436-9, 978-1-4244-1437-6
    Publication statusPublished - 2007
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
    Duration: 16 Sept 200719 Sept 2007


    ConferenceIEEE International Conference on Image Processing, ICIP 2007
    Abbreviated titleICIP 2007
    Country/TerritoryUnited States
    CitySan Antonio, TX


    • Machine vision
    • driver
    • distraction
    • SVM
    • camera
    • classification


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