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https://hdl.handle.net/2440/55341
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Type: | Conference paper |
Title: | Analyzing human movements from silhouettes using manifold learning |
Author: | Wang, L. Suter, D. |
Citation: | IEEE International Conference on Video and Signal Based Surveillance, Nov. 2006: pp.1-6 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2006 |
ISBN: | 0769526888 9780769526881 |
Conference Name: | IEEE Conference on Video and Signal Based Surveillance - AVSS (2006 : Sydney, Australia) |
Statement of Responsibility: | Liang Wang and David Suter |
Abstract: | A novel method for learning and recognizing sequential image data is proposed, and promising applications to vision-based human movement analysis are demonstrated. To find more compact representations of high-dimensional silhouette data, we exploit locality preserving projections (LPP) to achieve low-dimensional manifold embedding. Further, we present two kinds of methods to analyze and recognize learned motion manifolds. One is correlation matching based on the Hausdorrf distance, and the other is a probabilistic method using continuous hidden Markov models (HMM). Encouraging results are obtained in two representative experiments in the areas of human activity recognition and gait-based human identification. |
DOI: | 10.1109/AVSS.2006.25 |
Published version: | http://dx.doi.org/10.1109/avss.2006.25 |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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