Using Binary Decision Tree and Multiclass SVM for Human Gesture Recognition

Using Binary Decision Tree and Multiclass SVM for Human Gesture Recognition

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 distribu...

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Bibliographic Details
Published in:2013 International Conference on Information Science and Applications (ICISA) pp. 1 - 4
Main Authors: Juhee Oh, Taehyub Kim, Hyunki Hong
Format: Conference Proceeding
Language:English
Published: IEEE 01-06-2013
Subjects:
Decision trees
Gesture recognition
Hidden Markov models
Histograms
Robot sensing systems
Support vector machines
Trajectory
Online Access:Get full text
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Summary: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.
ISBN:1479906026
9781479906024
ISSN:2162-9048
DOI:10.1109/ICISA.2013.6579388