Paper Info
Title
Online gesture recognition from pose kernel learning and decision forests
Abstract
The recent popularization of real time depth sensors has diversified the potential applications of online gesture recognition to end-user natural user interface (NUI). This requires significant robustness of the gesture recognition to cope with the noisy data from the popular depth sensor, while the quality of the final NUI heavily depends on the recognition execution speed. This work introduces a method for real-time gesture recognition from a noisy skeleton stream, such as those extracted from Kinect depth sensors. Each pose is described using an angular representation of the skeleton joints. Those descriptors serve to identify key poses through a Support Vector Machine multi-class classifier, with a tailored pose kernel. The gesture is labeled on-the-fly from the key pose sequence with a decision forest, which naturally performs the gesture time control/warping and avoids the requirement for an initial or neutral pose. The proposed method runs in real time and its robustness is evaluated in several experiments.
Year
DOI
Venue
2014
10.1016/j.patrec.2013.10.005
Pattern Recognition Letters
Keywords
DocType
Volume
online gesture recognition,kinect depth sensor,decision forest,real time depth sensor,popular depth sensor,gesture time control,recognition execution speed,real-time gesture recognition,final nui,real time,gesture recognition,natural user interface
Journal
39,
ISSN
Citations 
PageRank 
0167-8655
22
0.80
References 
Authors
28
6
Name
Order
Citations
PageRank
Leandro Miranda1562.06
Thales Vieira2968.25
Dimas Martínez3955.90
Thomas Lewiner470043.70
Antonio W. Vieira5833.18
Mario F. M. Campos61069.17