Human action recognition from silhouettes using manifold learning and MDA
by Fa-wang Liu, Hong-bin Deng
International Journal of Modelling, Identification and Control (IJMIC), Vol. 12, No. 1/2, 2011

Abstract: In this paper, a method for human action recognition based on silhouette observations is presented. The methodology combines neighbourhood preserving embedding (NPE) based dimensionality reduction and multiple discriminant analysis (MDA) based action discrimination. Since depth is more robust to appearance variations and enables the spatial relationship to be explored easily for future multi-target interaction, a miniature stereo vision machine with three cameras is employed for generating high-resolution dense depth maps at video rate. The silhouettes of a moving person are extracted as the input features through fusing colour and depth information. Motivated by the observation that human activities are often located on a low-dimensional latent space, NPE is adopted to learn the intrinsic action manifold. When the class information is available, MDA is utilised to find a linear subspace which is optimal for discrimination. Experimental results show that our system can recognise human actions accurately with temporal, intra- and inter-person variations.

Online publication date: Sat, 21-Mar-2015

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 Modelling, Identification and Control (IJMIC):
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