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dc.contributor.authorZhou, H.en
dc.contributor.authorWang, L.en
dc.contributor.authorSuter, D.en
dc.identifier.citationProceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA., 2008: pp.1-4en
dc.description.abstractThis paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize the motion properties. GP classification is then used to learn and predict motion categories. Experimental results on two real-world state-of-the-art datasets show that the proposed approach is effective, and outperforms support vector machine (SVM).en
dc.description.statementofresponsibilityHang Zhou, Liang Wang and David Suteren
dc.relation.ispartofseriesInternational Conference on Pattern Recognitionen
dc.titleHuman motion recognition using gaussian processes classificationen
dc.typeConference paperen
dc.contributor.conferenceInternational Conference on Pattern Recognition (19th : 2008 : Florida)en
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]en
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Computer Science publications

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