Paper Info
Title
Head Pose Estimation with Combined 2D SIFT and 3D HOG Features
Abstract
In this paper, an approach is presented to estimate the 3D position and orientation of head from RGB and depth images captured by a commercial sensor Kinect. We use 2D Scale-invariant feature transform (SIFT) features together with 3D histogram of oriented gradients (HOG) features which are extracted in a pair of RGB and depth images captured synchronously, named SIFT-HOG features, to improve the robustness and accuracy of head pose estimation. We apply random forests to formulate pose estimation as a regression problem, due to their power for handling large training data and the high mapping speed. And then the mean-shift method is employed to refine the result obtained by the random forests. The experiment results demonstrate that our approach of head pose estimation is efficient.
Year
DOI
Venue
2013
10.1109/ICIG.2013.133
ICIG
Keywords
Field
DocType
hog features,commercial sensor,scale-invariant feature,oriented gradient,large training data,experiment result,depth image,mean-shift method,head pose estimation,high mapping speed,sift-hog feature,random forest,head,sift,random processes,vegetation,rgb,image sensors,feature extraction,regression analysis,scale invariant feature transform,estimation,random forests,pose estimation,data handling
Computer vision,Scale-invariant feature transform,Pattern recognition,Computer science,3D pose estimation,Feature extraction,Robustness (computer science),Pose,Histogram of oriented gradients,RGB color model,Artificial intelligence,Random forest
Conference
Citations 
PageRank 
References 
4
0.38
11
Authors
4
Name
Order
Citations
PageRank
Bingjie Wang152.42
Wei Liang210816.51
Yucheng Wang3112.23
Yan Liang456.17