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View-independent human action recognition based on multi-view action images and discriminant learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2013 IEEE 11th IVMSP Workshop: 3D Image/Video Technologies and Applications, IVMSP 2013 - Proceedings
DOIs
Publication statusPublished - 2013
Publication typeA4 Article in a conference publication
Event2013 IEEE 11th Workshop on 3D Image/Video Technologies and Applications, IVMSP 2013 - Seoul, Korea, Republic of
Duration: 10 Jun 201312 Jun 2013

Conference

Conference2013 IEEE 11th Workshop on 3D Image/Video Technologies and Applications, IVMSP 2013
CountryKorea, Republic of
CitySeoul
Period10/06/1312/06/13

Abstract

In this paper a novel view-independent human action recognition method is proposed. A multi-camera setup is used to capture the human body from different viewing angles. Actions are described by a novel action representation, the so-called multi-view action image (MVAI), which effectively addresses the camera viewpoint identification problem, i.e., the identification of the position of each camera with respect to the person's body. Linear Discriminant Analysis is applied on the MVAIs in order to to map actions to a discriminant feature space where actions are classified by using a simple nearest class centroid classification scheme. Experimental results denote the effectiveness of the proposed action recognition approach.

Keywords

  • Discriminant Learning, Human Action Recognition, Multi-camera Setup, Multi-view Action Images