Human action recognition using product manifold theory and procrustes shape analysis

Abstract

The shape space that is composed of the configurations matrices of landmarks extracted from a given video is represented by a product manifold. Therefore, we proposed new method that can recognize a human action using mathematical properties of product manifold and Procrustes shape analysis. In the first step, we factorize each volume images belonging to training dataset as a time ordering sequence of images, and we extract pre-shape configuration vector of landmarks from each frames consisting an image sequence. Then, we have obtained a random sample of pre-shape configuration vectors from all videos stored in tanning database by using similar procedure, and we compute mean shape vectors for random sample of extracted shape vectors. In the second step, in order to recognize the query human action video, we derive a sequence of the pre-shape configuration vectors from given query video, and we project each shape vector on the tangent space with respect to the pole taking on a sequence of the mean shape vectors corresponding with a target video. We recognize a query video as target video that can minimize the distance between two sequences of the pre-shape vectors and the mean shape vectors. We assess the performance of our method using Weizmann human action dataset. Experimental results reveal that the proposed method performs generally very well on this dataset.