Paper Info
Title
Design and implementation of a head-pose estimation system used with large-scale screens
Abstract
In this paper, we propose a novel head-pose estimation system for use with a large-scale screen to provide intelligent interaction with content. The head image of the user is captured from a RGB-D (red, green, blue pixel value, and depth data) camera connected to a large-scale display system. The head orientation of the user is then estimated from the RGB-D data by using the random regression forest algorithm. The random regression forest algorithm is a very powerful tool for generalization problems that does not suffer from overfitting. By using the head-pose estimation system, the user's region-of-interest (ROI) is found in a large-scale screen. After the ROI is found, various intelligent interactions with content can be possible. As future work, a hand gesture recognition system will be jointly connected with this head-pose estimation system in order to control the user's gestures more precisely in the ROI.
Year
DOI
Venue
2013
10.1109/GCCE.2013.6664827
GCCE
Keywords
Field
DocType
gesture recognition,image colour analysis,large screen displays,pose estimation,regression analysis,rgb-d camera,roi,hand gesture recognition system,head orientation,head-pose estimation system,intelligent interaction,large-scale display system,large-scale screen,random regression forest algorithm,region-of-interest,hand gesture recognition,head-pose estimation,random regression forest
Computer vision,Regression,Computer science,Regression analysis,Gesture,Gesture recognition,Pose,Artificial intelligence,Pixel,RGB color model,Overfitting
Conference
ISBN
Citations 
PageRank 
978-1-4799-0890-5
0
0.34
References 
Authors
3
5
Name
Order
Citations
PageRank
Sang-Heon Lee110522.48
Myoung-Kyu Sohn2337.17
Dong-Ju Kim36511.80
Hyunduk Kim44910.91
nuri ryu500.34