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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© 2006 Springer-Verlag Berlin Heidelberg
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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. https://doi.org/10.1007/11848035_61
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DOI: https://doi.org/10.1007/11848035_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
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