TY - GEN
T1 - Design and implementation of a head-pose estimation system used with large-scale screens
AU - Lee, Sang Heon
AU - Sohn, Myoung Kyu
AU - Kim, Dong Ju
AU - Kim, Hyunduk
AU - Ryu, Nuri
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Hand gesture recognition
KW - Head-pose estimation
KW - Random regression forest
KW - intelligent interaction
UR - http://www.scopus.com/inward/record.url?scp=84892630278&partnerID=8YFLogxK
U2 - 10.1109/GCCE.2013.6664827
DO - 10.1109/GCCE.2013.6664827
M3 - Conference contribution
AN - SCOPUS:84892630278
SN - 9781479908929
T3 - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
SP - 282
EP - 283
BT - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
T2 - 2013 IEEE 2nd Global Conference on Consumer Electronics, GCCE 2013
Y2 - 1 October 2013 through 4 October 2013
ER -