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Type: Conference paper
Title: Extrapolating learned manifolds for human activity recognition
Author: Chin, T.
Wang, L.
Schindler, K.
Suter, D.
Citation: Proceedings of the 14th IEEE International Conference on Image Processing, 2007, San Antonio, Texas, USA., 2007;. pp.381-384
Publisher: IEEE
Publisher Place: Online
Issue Date: 2007
Series/Report no.: IEEE International Conference on Image Processing ICIP
ISBN: 1424414377
ISSN: 1522-4880
Conference Name: IEEE International Conference on Image Processing (14th : 2007 : San Antonio, Texas)
Statement of
Tat-Jun Chin, Liang Wang, Schindler, K. and Suter, D.
Abstract: The problem of human activity recognition via visual stimuli can be approached using manifold learning, since the silhouette (binary) images of a person undergoing a smooth motion can be represented as a manifold in the image space. While manifold learning methods allow the characterization of the activity manifolds, performing activity recognition requires distinguishing between manifolds. This invariably involves the extrapolation of learned activity manifolds to new silhouettes -a task that is not fully addressed in the literature. This paper investigates and compares methods for the extrapolation of learned manifolds within the context of activity recognition. Also, the problem of obtaining dense samples for learning human silhouette manifolds is addressed.
Keywords: Human activity recognition
dense sampling
manifold extrapolation methods
manifold learning
DOI: 10.1109/ICIP.2007.4378971
Appears in Collections:Aurora harvest 5
Computer Science publications

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