Enhancing discriminability of randomized time warping for motion recognition
Lincon Sales de Souza, Bernardo Bentes Gatto, Kazuhiro Fukui · 2017
In this paper, we propose a framework of action sequence recognition by combining the representation of randomized time warping (RTW) with the enhanced Grassmann discriminant Analysis (eGDA). RTW is an extension of Dynamic time warping (DTW), and it has been shown to be effective for motion recognition, as it can effectively retain an actions temporal information by generating a low-dimensional subspace from a set of time elastic (TE) features of a video. On the other hand, the eGDA can use the concepts of generalized difference subspace and Grassmann manifold symbiotically to learn a discriminative manifold where video subspaces can be regarded as points. The main advantages of the proposed method are: removing common features between the actions which are not useful for discrimination, thus increasing the distance between subspaces of different classes, and reducing the distance between subspaces of the same class; and estimating a discriminative manifold even if there are few training data. We demonstrate the validity of the proposed method through experiments on motion recognition using two public datasets, namely, the Cambridge gesture database and the KTH action dataset.