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Journal of Electronic Imaging

Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies
Author(s): Eshed Ohn-Bar; Sujitha Martin; Mohan Trivedi
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Paper Abstract

We focus on vision-based hand activity analysis in the vehicular domain. The study is motivated by the overarching goal of understanding driver behavior, in particular as it relates to attentiveness and risk. First, the unique advantages and challenges for a nonintrusive, vision-based solution are reviewed. Next, two approaches for hand activity analysis, one relying on static (appearance only) cues and another on dynamic (motion) cues, are compared. The motion-cue-based hand detection uses temporally accumulated edges in order to maintain the most reliable and relevant motion information. The accumulated image is fitted with ellipses in order to produce the location of the hands. The method is used to identify three hand activity classes: (1) two hands on the wheel, (2) hand on the instrument panel, (3) hand on the gear shift. The static-cue-based method extracts features in each frame in order to learn a hand presence model for each of the three regions. A second-stage classifier (linear support vector machine) produces the final activity classification. Experimental evaluation with different users and environmental variations under real-world driving shows the promise of applying the proposed systems for both postanalysis of captured driving data as well as for real-time driver assistance.

Paper Details

Date Published: 21 October 2013
PDF: 11 pages
J. Electron. Imaging. 22(4) 041119 doi: 10.1117/1.JEI.22.4.041119
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Eshed Ohn-Bar, Univ. of California, San Diego (United States)
Sujitha Martin, Univ. of California, San Diego (United States)
Mohan Trivedi, Univ. of California, San Diego (United States)

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