Abstract
In this paper we present a robust motion recognition framework for both motion capture and RGB-D sensor data. We extract four different types of features and apply a temporal difference operation to form the final feature vector for each frame in the motion sequences. The frames are classified with the extreme learning machine, and the final class of an action is obtained by majority voting. We test our framework with both motion capture and Kinect data and compare the results of different features. The experiments show that our approach can accurately classify actions with both sources of data. For 40 actions of motion capture data, we achieve 92.7% classification accuracy with real-time performance.
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