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This paper presents a human action recognition system based on motion capture features and support vector machine (SVM). We use 43 optical markers distributing on body and extremities to track the movement of human actions. In our system 21 different types of action are recognized. Applying SVM for the recognition of human action the overall prediction accuracy achieves to 84.1% when using the three-fold...
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