Human Behavior Classification Using Multi-Class Relevance Vector Machine
B. Yogameena, S. Veera Lakshmi, M. Archana and S. Raju Abhaikumar
DOI : 10.3844/jcssp.2010.1021.1026
Journal of Computer Science
Volume 6, Issue 9
Problem statement: In computer vision and robotics, one of the typical tasks is to identify specific objects in an image and to determine each object’s position and orientation relative to coordinate system. This study presented a Multi-class Relevance Vector machine (RVM) classification algorithm which classifies different human poses from a single stationary camera for video surveillance applications. Approach: First the foreground blobs and their edges are obtained. Then the relevance vector machine classification scheme classified the normal and abnormal behavior. Results: The performance proposed by our method was compared with Support Vector Machine (SVM) and multi-class support vector machine. Experimental results showed the effectiveness of the method. Conclusion: It is evident that RVM has good accuracy and lesser computational than SVM.
© 2010 B. Yogameena, S. Veera Lakshmi, M. Archana and S. Raju Abhaikumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.