DYNAMIC SHAPE STYLE ANALYSIS: BILINEAR AND MULTILINEAR HUMAN IDENTIFICATION WITH TEMPORAL NORMALIZATION

Title
DYNAMIC SHAPE STYLE ANALYSIS: BILINEAR AND MULTILINEAR HUMAN IDENTIFICATION WITH TEMPORAL NORMALIZATION
Author(s)
이찬수아메드 엘가멜[아메드 엘가멜]
Keywords
GAIT RECOGNITION; MODELS; APPEARANCE; APPROXIMATION; MANIFOLD
Issue Date
201011
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Citation
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.24, no.7, pp.1133 - 1157
Abstract
Modeling and analyzing the dynamic shape of human motion is a challenging task owing to temporal variations in the shape and multiple sources of observed shape variations such as viewpoint, motion speed, clothing, etc. We present a new framework for dynamic shape analysis based on temporal normalization and factorized shape style analysis. Using a nonlinear generative model with motion manifold embedding in a low-dimensional space, we detect cycles of periodic motion like gait in different views and synthesize temporally-aligned shape sequences from the same type of motion at different speeds. The bilinear analysis of temporally-aligned shape sequences decomposes dynamic motion into time-invariant shape style factors and time-dependent motion factors. We extend the bilinear model into a tensor shape model, a multilinear decomposition of dynamic shape sequences for view-invariant shape style representations. The shape style is a view-invariant, time-invariant, and speed-invariant shape signature and is used as a feature vector for human identification. The shape style can be adapted to new environmental conditions by iterative estimation of style and content factors to reflect new observation conditions. We present the experimental results of gait recognition using the CMU Mobo gait database and the USF gait challenging database.
URI
http://hdl.handle.net/YU.REPOSITORY/23378http://dx.doi.org/10.1142/S0218001410008330
ISSN
0218-0014
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공과대학 > 전자공학과 > Articles
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