A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views

Chaaraoui, Alexandros and Florez Revuelta, Francisco (2014) A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views. International Scholarly Research Notices, 2014(547069), ISSN (online) 2356-7872

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Abstract

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Item Type: Article
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017)
Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
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Depositing User: Francisco Florez Revuelta
Date Deposited: 18 Mar 2015 20:21
Last Modified: 13 Jul 2016 11:21
DOI: https://doi.org/10.1155/2014/547069
URI: http://eprints.kingston.ac.uk/id/eprint/30261

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