Driver distraction represents a major safety problem in the United States. Naturalistic driving data, such as SHRP 2 Naturalistic Driving Study (NDS) data, provide a new window into driver behavior that promises a deeper understanding than was previously possible. Unfortunately, the current practice of manual coding is infeasible for large data sets such as SHRP 2 NDS, which contains millions of hours of video. Computer vision algorithms have the potential to automatically code SHRP 2 NDS videos. However, existing algorithms are brittle in the presence of challenges such as low video quality, underexposure and overexposure, driver occlusion, nonfrontal faces, and unpredictable and significant illumination changes, which are all substantially present in SHRP 2 NDS videos. This paper presents and evaluates algorithms developed to quantify high-level features pertinent to driver distraction and engagement in challenging videos like those in SHRP 2 NDS. Specifically, a novel three-stage video analysis system is presented for tracking head position and estimating head pose and eye and mouth states. The accuracy of the new head pose estimation module is competitive with the state of the art on publicly available data sets and produces good qualitative results on SHRP 2 NDS videos.


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    Title :

    Automatic Driver Head State Estimation in Challenging Naturalistic Driving Videos


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2017-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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