Action recognition using joint coordinates of 3D skeleton data

We propose an action recognition technique using the 3D skeleton model of human without compromising the identity of the person. The skeleton model is defined as a set of 3D joint (e.g. knee or hip joint) coordinates obtained from the Kinect. The low frequency sensor noise in estimating the joint coordinates is removed after modeling the covariance matrix of the joint coordinates as a function of variance of individual joint coordinates. We determine a range for the threshold of this covariance matrix to detect active joints defining an action. Since, a sparse set of active joint coordinates is enough to represent an action, we map these coordinates to lower dimensional linear manifold before training using an SVM classifier. The recognition rate using our proposed approach outperforms competing approaches by at least 2%.