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Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition

Souza, LS; Gatto, BB; Xue, J-H; Fukui, K; (2020) Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition. Pattern Recognition , 97 , Article 107028. 10.1016/j.patcog.2019.107028. Green open access

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Abstract

This paper proposes a framework for classifying motion sequences, by extending the framework of Grassmann discriminant analysis (GDA). A problem of GDA is that its discriminant space is not necessarily optimal. This limitation becomes even more prominent when utilizing the subspace representation of randomized time warping (RTW). RTW is a sequence representation that can eectively model a motion’s temporal information by a low-dimensional subspace, simplifying the problem of comparing two sequences to that of comparing two subspaces. The key idea of the proposed enhanced GDA is projecting class subspaces onto a generalized dierence subspace before mapping them on a Grassmann manifold. The GDS projection can remove overlapping components of the subspaces in the vector space, nearly orthogonalizing them. Consequently, a dictionary of orthogonalized class subspaces produces a set of more discriminant data points in the Grassmann manifold, in comparison with the original set. This set of data points can further enhance the discriminant ability of GDA. We demonstrate the validity of the proposed framework, RTW+eGDA, through experiments on motion recognition using the publicly available Cambridge gesture, KTH action, and UCF sports datasets.

Type: Article
Title: Enhanced Grassmann discriminant analysis with randomized time warping for motion recognition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.patcog.2019.107028
Publisher version: https://doi.org/10.1016/j.patcog.2019.107028
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: enhanced GDA, randomized time warping, motion recognition
UCL classification: UCL
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: https://discovery.ucl.ac.uk/id/eprint/10081174
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