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Received: 2008-05-22

Revision Accepted: 2008-10-18

Crosschecked: 2009-04-10

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.6 P.810-819


Bi-dimension decomposed hidden Markov models for multi-person activity recognition

Author(s):  Wei-dong ZHANG, Feng CHEN, Wen-li XU

Affiliation(s):  Department of Automation, Tsinghua University, Beijing 100084, China

Corresponding email(s):   zwd03@mails.tsinghua.edu.cn

Key Words:  Multi-channel setting, Hierarchical modeling, Hidden Markov model, Activity recognition

Wei-dong ZHANG, Feng CHEN, Wen-li XU. Bi-dimension decomposed hidden Markov models for multi-person activity recognition[J]. Journal of Zhejiang University Science A, 2009, 10(6): 810-819.

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author="Wei-dong ZHANG, Feng CHEN, Wen-li XU",
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%T Bi-dimension decomposed hidden Markov models for multi-person activity recognition
%A Wei-dong ZHANG
%A Feng CHEN
%A Wen-li XU
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T1 - Bi-dimension decomposed hidden Markov models for multi-person activity recognition
A1 - Wei-dong ZHANG
A1 - Feng CHEN
A1 - Wen-li XU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 6
SP - 810
EP - 819
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Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820388

We present a novel model for recognizing long-term complex activities involving multiple persons. The proposed model, named ‘decomposed hidden Markov model’ (DHMM), combines spatial decomposition and hierarchical abstraction to capture multi-modal, long-term dependent and multi-scale characteristics of activities. Decomposition in space and time offers conceptual advantages of compaction and clarity, and greatly reduces the size of state space as well as the number of parameters. DHMMs are efficient even when the number of persons is variable. We also introduce an efficient approximation algorithm for inference and parameter estimation. Experiments on multi-person activities and multi-modal individual activities demonstrate that DHMMs are more efficient and reliable than familiar models, such as coupled HMMs, hierarchical HMMs, and multi-observation HMMs.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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