Understanding human behavior using a language modeling approach

Yu Ming Liang, Sheng Wen Shih, Arthur Chun Chieh Shih, Hong Yuan Mark Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visual analysis of human behavior has generated considerable interest in the field of computer vision because of the wide spectrum of potential applications. In this paper, we present a language modeling framework for understanding human behavior. The proposed framework consists of two modules: the key posture selection module, and the variable-length Markov model (VLMM) behavior recognition module. A key posture selection algorithm is developed based on the shape context matching technique. A codebook is then constructed with the computed key postures and used to convert input image sequences into training symbol sequences or recognition symbol sequences. Finally, a VLMM is applied to learn and recognize the constructed symbol sequences corresponding to human behavior patterns. Experiments on real data demonstrate the efficacy of the proposed system.

Original languageEnglish
Title of host publicationProceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
Pages331-334
Number of pages4
DOIs
Publication statusPublished - 2006
Event2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006 - Pasadena, CA, United States
Duration: Dec 18 2006Dec 20 2006

Publication series

NameProceedings - 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006

Conference

Conference2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006
Country/TerritoryUnited States
CityPasadena, CA
Period12/18/0612/20/06

ASJC Scopus subject areas

  • General Computer Science

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