Particle filter for targets tracking with motion model

Grantham K.H. Pang, King Lun Tommy Choy

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Real-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.
Original languageEnglish
Title of host publication2013 IEEE 8th International Conference on Industrial and Information Systems, ICIIS 2013 - Conference Proceedings
Pages128-132
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2013
Event2013 IEEE 8th International Conference on Industrial and Information Systems, ICIIS 2013 - Peradeniya, United States
Duration: 17 Dec 201320 Dec 2013

Conference

Conference2013 IEEE 8th International Conference on Industrial and Information Systems, ICIIS 2013
CountryUnited States
CityPeradeniya
Period17/12/1320/12/13

Keywords

  • kernel density estimation
  • particle filter
  • Target tracking

ASJC Scopus subject areas

  • Information Systems

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