On the use of advanced correlation filters for human posture recognition

Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad, Hafizah Husain, Andrew Teoh Beng Jin

Research output: Contribution to journalArticle

Abstract

This study affords the method of using advance correlation filters in human posture recognition task. Two type of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89% compared to MACE filter which attained 42%.

Original languageEnglish
Pages (from-to)2947-2956
Number of pages10
JournalJournal of Applied Sciences
Volume7
Issue number20
Publication statusPublished - 15 Oct 2007

Keywords

  • Advanced correlation filters
  • Posture
  • Recognition

ASJC Scopus subject areas

  • General

Cite this

On the use of advanced correlation filters for human posture recognition. / Tahir, Nooritawati Md; Hussain, Aini; Abdul Samad, Salina; Husain, Hafizah; Jin, Andrew Teoh Beng.

In: Journal of Applied Sciences, Vol. 7, No. 20, 15.10.2007, p. 2947-2956.

Research output: Contribution to journalArticle

Tahir, Nooritawati Md ; Hussain, Aini ; Abdul Samad, Salina ; Husain, Hafizah ; Jin, Andrew Teoh Beng. / On the use of advanced correlation filters for human posture recognition. In: Journal of Applied Sciences. 2007 ; Vol. 7, No. 20. pp. 2947-2956.
@article{08e28be051db409ab6399a1b0cbce87d,
title = "On the use of advanced correlation filters for human posture recognition",
abstract = "This study affords the method of using advance correlation filters in human posture recognition task. Two type of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89{\%} compared to MACE filter which attained 42{\%}.",
keywords = "Advanced correlation filters, Posture, Recognition",
author = "Tahir, {Nooritawati Md} and Aini Hussain and {Abdul Samad}, Salina and Hafizah Husain and Jin, {Andrew Teoh Beng}",
year = "2007",
month = "10",
day = "15",
language = "English",
volume = "7",
pages = "2947--2956",
journal = "Journal of Applied Sciences",
issn = "1812-5654",
publisher = "Asian Network for Scientific Information",
number = "20",

}

TY - JOUR

T1 - On the use of advanced correlation filters for human posture recognition

AU - Tahir, Nooritawati Md

AU - Hussain, Aini

AU - Abdul Samad, Salina

AU - Husain, Hafizah

AU - Jin, Andrew Teoh Beng

PY - 2007/10/15

Y1 - 2007/10/15

N2 - This study affords the method of using advance correlation filters in human posture recognition task. Two type of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89% compared to MACE filter which attained 42%.

AB - This study affords the method of using advance correlation filters in human posture recognition task. Two type of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89% compared to MACE filter which attained 42%.

KW - Advanced correlation filters

KW - Posture

KW - Recognition

UR - http://www.scopus.com/inward/record.url?scp=36849090801&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=36849090801&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:36849090801

VL - 7

SP - 2947

EP - 2956

JO - Journal of Applied Sciences

JF - Journal of Applied Sciences

SN - 1812-5654

IS - 20

ER -