Recognizing involuntary actions from 3D skeleton data using body states

Abstract

Human action recognition has been one of the most active fields of research in computer vision over the last years. Two-dimensional action recognition methods face serious challenges such as occlusion and missing the third dimension of data. The development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method for action recognition that uses temporal 3D skeletal Kinect data. This method introduces the definition of body states; then, every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods with a recognition (recall) rate of 88.64% for eight different actions and up to 96.18% for classifying the class of all fall actions versus normal actions.