Human Action Classification Using SVM_2K Classifier on Motion Features

Hongying Meng; Nick Pears; Chris Bailey

doi:10.1007/11848035_61

Human Action Classification Using SVM_2K Classifier on Motion Features

Hongying Meng, Nick Pears, Chris Bailey

Computer Science

Research output: Contribution to conference › Paper

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.

Original language	Undefined/Unknown
Pages	458-465
DOIs	https://doi.org/10.1007/11848035_61
Publication status	Published - 2006

Access to Document

10.1007/11848035_61

http://dx.doi.org/10.1007/11848035_61

Cite this

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title = "Human Action Classification Using SVM_2K Classifier on Motion Features",

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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AB - 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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