Online human gesture recognition from motion data streams
Files
(Restricted Access)
Date
2013
Authors
Zhao, X.
Li, X.
Pang, C.
Zhu, X.
Sheng, Q.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 2013 21st ACM Multimedia Conference, 2013 / pp.23-32
Statement of Responsibility
Xin Zhao, Xue Li, Chaoyi Pang, Xiaofeng Zhu, Quan Z. Sheng
Conference Name
International Conference on Multimedia (21st : 2013 : Barcelona)
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2013 ACM