Human Action Classification Using SVM_2K Classifier on Motion Features

  • Hongying Meng
  • Nick Pears
  • Chris Bailey
Conference paper

DOI: 10.1007/11848035_61

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)
Cite this paper as:
Meng H., Pears N., Bailey C. (2006) Human Action Classification Using SVM_2K Classifier on Motion Features. In: Gunsel B., Jain A.K., Tekalp A.M., Sankur B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg

Abstract

In this paper, we study the human action classification problem based on motion features directly extracted from video. In order to implement a fast classification system, we select simple features that can be obtained from non-intensive computation. We also introduce the new SVM_2K classifier that can achieve improved performance over a standard SVM by combining two types of motion feature vector together. After learning, classification can be implemented very quickly because SVM_2K is a linear classifier. Experimental results demonstrate the method to be efficient and may be used in real-time human action classification systems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongying Meng
    • 1
  • Nick Pears
    • 1
  • Chris Bailey
    • 1
  1. 1.Department of Computer ScienceThe University of YorkYorkUK

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