RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization
© B. Yogameena et al. 2009
Received: 1 February 2009
Accepted: 26 August 2009
Published: 30 November 2009
Detection of abnormal human actions in the crowd has become a critical problem in video surveillance applications like terrorist attacks. This paper proposes a real-time video surveillance system which is capable of classifying normal and abnormal actions of individuals in a crowd. The abnormal actions of human such as running, jumping, waving hand, bending, walking and fighting with each other in a crowded environment are considered. In this paper, Relevance Vector Machine (RVM) is used to classify the abnormal actions of an individual in the crowd based on the results obtained from projection and skeletonization methods. Experimental results on benchmark datasets demonstrate that the proposed system is robust and efficient. A comparative study of classification accuracy between Relevance Vector Machine and Support Vector Machine (SVM) classification is also presented.
To access the full article, please see PDF.
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.