Adaptive target color model updating for visual tracking using particle filter

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

10 Scopus citations

Abstract

Color histogram distribution is robust against non-rigidity, scale and rotation. Color-based particle filtering is one of the most successful object tracking paradigms. But visual tracking in real world conditions such as changing illumination and poses is still a challenging job. In this paper, we develop an color histogram based particle filter tracker with adaptive target model updating. The proposed approach adds two auxiliary variables in the particle state space. These two auxiliary variables control the updating speed of the color observation mode, and are also estimated in the Sequential Monte Carlo framework. This algorithm has been tested on real image sequences and accurate tracking result has been achieved.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages3105-3109
Number of pages5
DOIs
StatePublished - 2004
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: 10 Oct 200413 Oct 2004

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume4
ISSN (Print)1062-922X

Conference

Conference2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Country/TerritoryNetherlands
CityThe Hague
Period10/10/0413/10/04

Keywords

  • Color histogram
  • Particle filtering
  • Visual tracking

Fingerprint

Dive into the research topics of 'Adaptive target color model updating for visual tracking using particle filter'. Together they form a unique fingerprint.

Cite this