By Topic

A novel eigenspace-based method for human action recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

This paper describes a new robust appearance-based method for representing and recognizing human behaviours using the eigenspace technique. This method has three main advantages over the existing appearance-based methods. First, the centering of the human-body blob, in each background-subtracted video frame, together with the use of an incremental procedure for compression, have made the extraction of the motion features limited to the smallest possible area in the image. Second, a learning strategy based on the eigen-space technique is employed for dimensionality reduction using the Linear Discriminant Analysis algorithm (LDA), while providing maximum separability between classes. Third, data retrieving has been greatly enhanced by using a directed acyclic graph (DAG) structure based on the Euclidean distance between projected data. The system has been tested using a large number of training motion videos partitioned into 6 human behaviours (boxing, hand-clapping, hand-waving, jogging, running, and walking) captured for 25 different persons in 2 different scenarios (indoor and outdoor). The experimental results are very good, showing a high performance level of the proposed method.

Published in:

Digital Information Management (ICDIM), 2010 Fifth International Conference on

Date of Conference:

5-8 July 2010