Human behavior classification using thinning algorithm and support vector machine

M. Rahmat Widyanto; Sukmawati Nur Endah; Kaoru Hirota

doi:10.20965/jaciii.2010.p0028

Human behavior classification using thinning algorithm and support vector machine

M. Rahmat Widyanto
, Sukmawati Nur Endah
, Kaoru Hirota

University of Indonesia
Universitas Diponegoro
Tokyo Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

This paper proposes a skeleton-based human behavior classification system using thinning algorithm and Support Vector Machine (SVM). The proposed system consists of two phases, skeletonization phase where main human body part is constructed using thinning algorithm, and classification phase where the skeleton constructed by previous phase is classified into certain human behavior pose using SVM. Experiment using 44 training and 44 testing data of real human poses shows that the system achieves 81.06% accuracy. This system can be further developed for early detection of criminal action.

Original language	English
Pages (from-to)	28-33
Number of pages	6
Journal	Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume	14
Issue number	1
DOIs	https://doi.org/10.20965/jaciii.2010.p0028
Publication status	Published - Jan 2010
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Support vector machine
Thinning algorithm

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10.20965/jaciii.2010.p0028

Cite this

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title = "Human behavior classification using thinning algorithm and support vector machine",

abstract = "This paper proposes a skeleton-based human behavior classification system using thinning algorithm and Support Vector Machine (SVM). The proposed system consists of two phases, skeletonization phase where main human body part is constructed using thinning algorithm, and classification phase where the skeleton constructed by previous phase is classified into certain human behavior pose using SVM. Experiment using 44 training and 44 testing data of real human poses shows that the system achieves 81.06\% accuracy. This system can be further developed for early detection of criminal action.",

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AB - This paper proposes a skeleton-based human behavior classification system using thinning algorithm and Support Vector Machine (SVM). The proposed system consists of two phases, skeletonization phase where main human body part is constructed using thinning algorithm, and classification phase where the skeleton constructed by previous phase is classified into certain human behavior pose using SVM. Experiment using 44 training and 44 testing data of real human poses shows that the system achieves 81.06% accuracy. This system can be further developed for early detection of criminal action.

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KW - Thinning algorithm

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Human behavior classification using thinning algorithm and support vector machine

Abstract

UN SDGs

Keywords

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