A novel automatic motion capture data recognition method based on statistics learning and subspace
by Jian Xiang, Hongli Zhu
International Journal of Computer Applications in Technology (IJCAT), Vol. 38, No. 1/2/3, 2010

Abstract: In this paper, we propose a motion recognition method based on motion capture data. To recognise motion type, a generalised Isomap non-linear dimension reduction based on Radius Basis Function (RBF) networks and feature extraction is used to project original motion data into low-dimensional subspace. Then, some motion-type classifiers are learned for each human's joint in subspace. Then, we use ensemble reinforcement learning to enhance learning results. Experimental results show that our methods are effective for 3D human motion recognition and control.

Online publication date: Fri, 16-Jul-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com