Multiple Timescale Recurrent Neural Network with Slow Feature Analysis for Efficient Motion Recognition

Multiple Timescale Recurrent Neural Network MTRNN model is a useful tool to learn and regenerate various kinds of action. In this paper, we use MTRNN as a dynamic model to analyze different human motions. Prediction error from dynamic model is used to classify different human actions. However, it is difficult to fully cover the human actions depending on the speed using dynamic model. In order to overcome the limitation of dynamic model, we considered Slow Feature analysis SFA which is used to extract the unique slow features from human actions data. In order to make input training data, we obtain 3 kinds of human actions by using KINECT. 3 dimensional slow feature data is be extracted by using SFA and those SFA feature data are used as the input of MTRNN for classification. The experiment results show that our proposed model performs better than the traditional model.

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