Using Binary Decision Tree and Multiclass SVM for Human Gesture Recognition

Author

Juhee Oh ; Taehyub Kim ; Hyunki Hong

Author_Institution

Dept. of Imaging Sci. & Arts, Chung-Ang Univ., Seoul, South Korea

fYear

2013

fDate

24-26 June 2013

Firstpage

1

Lastpage

4

Abstract

This paper presents a novel method to recognize the human gesture using binary decision tree and Multi-class Support Vector Machine (MCSVM). In a learning stage, 3D trajectory of the human gesture by a kinect sensor is assigned into the tree node of the binary decision tree according to its distribution property. The user´s gesture trajectory is resampled and normalized, and we extract the chain code histogram at a regular interval. After training MCSVM in each node, we are able to recognize the human gestures.

Keywords

binary decision diagrams; decision trees; gesture recognition; image sampling; image sensors; learning (artificial intelligence); support vector machines; 3D trajectory; MCSVM training; binary decision tree node; chain code histogram extraction; distribution property; human gesture recognition; kinect sensor; learning stage; multiclass SVM; user gesture trajectory normalization; user gesture trajectory resampling; Decision trees; Gesture recognition; Hidden Markov models; Histograms; Robot sensing systems; Support vector machines; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Applications (ICISA), 2013 International Conference on

Conference_Location

Suwon

Print_ISBN

978-1-4799-0602-4

Type

conf

DOI

10.1109/ICISA.2013.6579388

Filename

6579388