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โœฆ   LIBER   โœฆ

Learning Variable-Length Markov Models of Behavior

โœ Scribed by Aphrodite Galata; Neil Johnson; David Hogg

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
264 KB
Volume
81
Category
Article
ISSN
1077-3142

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โœฆ Synopsis

In recent years there has been an increased interest in the modeling and recognition of human activities involving highly structured and semantically rich behavior such as dance, aerobics, and sign language. A novel approach for automatically acquiring stochastic models of the high-level structure of an activity without the assumption of any prior knowledge is presented. The process involves temporal segmentation into plausible atomic behavior components and the use of variable-length Markov models for the efficient representation of behaviors. Experimental results that demonstrate the synthesis of realistic sample behaviors and the performance of models for longterm temporal prediction are presented.

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