Action Recognition by Multiple Features and Hyper-sphere Multi-class SVM

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Show simple item record Liu, Jia en_US Yang, Jie en_US Zhang, Yi en_US He, Sean en_US
dc.contributor.editor Mejdat Aetin, Kim Boyer and Seong-Whan Lee - ICPR 2010 Technical Program Chairs en_US 2012-02-02T11:08:07Z 2012-02-02T11:08:07Z 2010 en_US
dc.identifier 2009007669 en_US
dc.identifier.citation Liu Jia et al. 2010, 'Action Recognition by Multiple Features and Hyper-sphere Multi-class SVM', , IEEE Computer Society, Istanbul Turkey, , pp. 3744-3747. en_US
dc.identifier.issn 978-1-4244-7542-1 en_US
dc.identifier.other E1 en_US
dc.description.abstract In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single features based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids and 2-D SIFT), and ii) the higher order statistical models of interest points, which aims to capture the global information of the actor. We construct video presentation in terms of local space time features and global features and integrate such representations with hper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed approach is effective. An additional experiment shows that using both local and global features provides a richer representation of human action when compared to the use of a single feature type. en_US
dc.language English en_US
dc.publisher IEEE Computer Society en_US
dc.relation.isbasedon en_US
dc.title Action Recognition by Multiple Features and Hyper-sphere Multi-class SVM en_US
dc.parent Proceedings: 2010 20th International Conference Pattern Recognition (ICPR 2010) en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Istanbul Turkey en_US
dc.identifier.startpage 3744 en_US
dc.identifier.endpage 3747 en_US FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000063898 en_US
dc.personcode 0000027379 en_US
dc.personcode 0000063899 en_US
dc.personcode 990421 en_US
dc.percentage 100 en_US Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference Pattern Recognition en_US 20100823 en_US
dc.location.activity Istanbul Turkey en_US
dc.description.keywords human action recognition; multiple features; hyper-sphere Multi-class SVM en_US
dc.staffid 990421 en_US

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