Cited 0 time in webofscience Cited 0 time in scopus

Real-time head pose estimation using weighted random forests

Title
Real-time head pose estimation using weighted random forests
Authors
Kim, HyundukSohn, Myoung-KyuKim, Dong-JuRyu, Nuri
DGIST Authors
Kim, Hyunduk; Sohn, Myoung-Kyu; Kim, Dong-Ju; Ryu, Nuri
Issue Date
2014
Citation
, 8733, 554-562
Type
Conference
Article Type
Article
ISSN
0302-9743
Abstract
In this paper we proposed to real-time head pose estimation based on weighted random forests. In order to make real-time and accurate classification, weighted random forests classifier, was employed. In the training process, we calculate accuracy estimation using preselected out-of-bag data. The accuracy estimation determine the weight vector in each tree, and improve the accuracy of classification when the testing process. Moreover, in order to make robust to illumination variance, binary pattern operators were used for preprocessing. Experiments on public databases show the advantages of this method over other algorithm in terms of accuracy and illumination invariance. ⓒ Springer International Publishing Switzerland 2014.
URI
http://hdl.handle.net/20.500.11750/3787
DOI
10.1007/978-3-319-11289-3_56
Publisher
Springer Verlag
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


qrcode mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE