Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/83773
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Type: Conference paper
Title: Online human gesture recognition from motion data streams
Author: Zhao, X.
Li, X.
Pang, C.
Zhu, X.
Sheng, Q.
Citation: Proceedings of the 2013 21st ACM Multimedia Conference, 2013 / pp.23-32
Publisher: Association for Computing Machinery
Publisher Place: Spain
Issue Date: 2013
ISBN: 9781450324045
Conference Name: International Conference on Multimedia (21st : 2013 : Barcelona)
Statement of
Responsibility: 
Xin Zhao, Xue Li, Chaoyi Pang, Xiaofeng Zhu, Quan Z. Sheng
Abstract: Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the state-of-the-art approaches.
Keywords: Gesture Recognition; Feature Extraction; Depth Camera
Rights: Copyright 2013 ACM
RMID: 0020132950
DOI: 10.1145/2502081.2502103
Appears in Collections:Computer Science publications

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