Paper Info
Title
Head, Eye, and Hand Patterns for Driver Activity Recognition
Abstract
In this paper, a multiview, multimodal vision framework is proposed in order to characterize driver activity based on head, eye, and hand cues. Leveraging the three types of cues allows for a richer description of the driver's state and for improved activity detection performance. First, regions of interest are extracted from two videos, one observing the driver's hands and one the driver's head. Next, hand location hypotheses are generated and integrated with a head pose and facial landmark module in order to classify driver activity into three states: wheel region interaction with two hands on the wheel, gear region activity, or instrument cluster region activity. The method is evaluated on a video dataset captured in on-road settings.
Year
DOI
Venue
2014
10.1109/ICPR.2014.124
Pattern Recognition
Keywords
Field
DocType
computer vision,face recognition,pattern clustering,pose estimation,traffic engineering computing,activity detection performance improvement,driver activity recognition,eye patterns,facial landmark module,gear region activity,hand location hypotheses,hand patterns,head patterns,head pose,instrument cluster region activity,multimodal vision framework,multiview vision framework,on-road settings,regions of interest,video dataset,wheel region interaction
Computer vision,Activity recognition,Pattern recognition,Computer science,Activity detection,Artificial intelligence,Landmark
Conference
ISSN
Citations 
PageRank 
1051-4651
24
1.04
References 
Authors
12
4
Name
Order
Citations
PageRank
Eshed Ohn-Bar1742.93
Sujitha Martin2281.82
Ashish Tawari321916.07
Mohan M. Trivedi46564475.50