Human Action Classification Using SVM_2K Classifier on Motion Features

Hongying Meng, Nick Pears, Chris Bailey

Research output: Contribution to conferencePaper


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 languageUndefined/Unknown
Publication statusPublished - 2006

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