Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/55348
Type: | Conference paper |
Title: | Human motion recognition using gaussian processes classification |
Author: | Zhou, H. Wang, L. Suter, D. |
Citation: | Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA., 2008: pp.1-4 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2008 |
Series/Report no.: | International Conference on Pattern Recognition |
ISBN: | 9781424421749 |
ISSN: | 1051-4651 |
Conference Name: | International Conference on Pattern Recognition (19th : 2008 : Florida) |
Statement of Responsibility: | Hang Zhou, Liang Wang and David Suter |
Abstract: | This 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). |
Description (link): | http://dx.doi.org/10.1109/ICPR.2008.4761140 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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