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Randomized time warping for motion recognition

Suryanto, CH; Xue, J-H; Fukui, K; (2016) Randomized time warping for motion recognition. Image and Vision Computing , 54 pp. 1-11. 10.1016/j.imavis.2016.07.003. Green open access

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Abstract

Dynamic time warping (DTW) has been widely used for the alignment and comparison of two sequential patterns. In DTW, dynamic programming is used to avoid an exhaustive search for the alignment. In this paper, we propose a randomized extension of the DTW concept, termed randomized time warping (RTW), for motion recognition. RTW generates time elastic (TE) features by randomly sampling the sequential data while retaining the temporal information. A set of TE features is represented by a low-dimensional subspace, called the sequence hypothesis (Hypo) subspace, and the similarity between two sequential patterns is defined by the canonical angles between the two corresponding Hypo subspaces. In essence, RTW simultaneously computes multiple degrees of similarities between a number of warped patterns' pair candidates, while in practice, RTW generalizes the Hankel matrix commonly used in modeling of system dynamics. We demonstrate the applicability of RTW through experiments on gesture recognition using three public datasets, namely, the Cambridge gesture database, a subset of the one-shot-learning dataset from the ChaLearn Gesture Challenge, and the KTH action dataset.

Type: Article
Title: Randomized time warping for motion recognition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.imavis.2016.07.003
Publisher version: http://dx.doi.org/10.1016/j.imavis.2016.07.003
Language: English
Additional information: Copyright © 2016 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: Science & Technology, Technology, Physical Sciences, Computer Science, Artificial Intelligence, Computer Science, Software Engineering, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Optics, Computer Science, Engineering, Feature extraction, Dynamic time warping, Subspace method, Hankel matrix, Motion recognition, Gesture Recognition, Subspace
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1508467
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