Background removal in vision servo system using Gaussian mixture model framework

Tzung Min Su*, Jwu-Sheng Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Finding targets in a complex background is important for visual servo systems. From feedback control perspective, it represents reliable sensor information to prevent the servo systems from tracking a wrong target. Prior to target identification, it is necessary to localize the target within the picture so as to enhance the identification accuracy. Most image processing methods applied for target localization dealt with static pictures. In other words, information regarding the temporal behavior of the video sequence is seldom utilized. In this paper, a statistical framework using Gaussian Mixture Model (GMM) is investigated to localize a moving target without knowing the information about the target. Then, support vector machine (SVM) is applied to be a classifier to identify the moving target. A vision servo system is constructed by the scheme that incorporates Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). Experimental results are shown to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationConference Proceedings - 2004 IEEE International Conference on Networking, Sensing and Control
Pages70-75
Number of pages6
DOIs
StatePublished - 28 Jun 2004
EventConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control - Taipei, Taiwan
Duration: 21 Mar 200423 Mar 2004

Publication series

NameConference Proceeding - IEEE International Conference on Networking, Sensing and Control
Volume1

Conference

ConferenceConference Proceeding - 2004 IEEE International Conference on Networking, Sensing and Control
CountryTaiwan
CityTaipei
Period21/03/0423/03/04

Keywords

  • Face Detection
  • Face Tracking
  • Gaussian Mixture Model (GMM)
  • Kalman filter
  • Support Vector Machine (SVM)

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