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Proceedings Paper

Human action classification using procrustes shape theory
Author(s): Wanhyun Cho; Sangkyoon Kim; Soonyoung Park; Myungeun Lee
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Paper Abstract

In this paper, we propose new method that can classify a human action using Procrustes shape theory. First, we extract a pre-shape configuration vector of landmarks from each frame of an image sequence representing an arbitrary human action, and then we have derived the Procrustes fit vector for pre-shape configuration vector. Second, we extract a set of pre-shape vectors from tanning sample stored at database, and we compute a Procrustes mean shape vector for these preshape vectors. Third, we extract a sequence of the pre-shape vectors from input video, and we project this sequence of pre-shape vectors on the tangent space with respect to the pole taking as a sequence of mean shape vectors corresponding with a target video. And we calculate the Procrustes distance between two sequences of the projection pre-shape vectors on the tangent space and the mean shape vectors. Finally, we classify the input video into the human action class with minimum Procrustes distance. We assess a performance of the proposed method using one public dataset, namely Weizmann human action dataset. Experimental results reveal that the proposed method performs very good on this dataset.

Paper Details

Date Published: 27 February 2015
PDF: 7 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050T (27 February 2015); doi: 10.1117/12.2083161
Show Author Affiliations
Wanhyun Cho, Chonnam National Univ. (Korea, Republic of)
Sangkyoon Kim, Mokpo National Univ. (Korea, Republic of)
Soonyoung Park, Mokpo National Univ. (Korea, Republic of)
Myungeun Lee, Seoul National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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