21 October 2013 Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies
Author Affiliations +
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.
© 2013 SPIE and IS&T
Eshed Ohn-Bar, Sujitha Martin, Mohan Trivedi, "Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies," Journal of Electronic Imaging 22(4), 041119 (21 October 2013). https://doi.org/10.1117/1.JEI.22.4.041119 . Submission:
JOURNAL ARTICLE
11 PAGES


SHARE
Back to Top