On the use of joint estimation in particle filters for object tracking in video

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Object tracking is an important problem, whose effective solution is crucial to lot of applications. Though several methods have been proposed in the literature, they fail when the object of interest does not conform to a specific process model. Also, current methods have to be tuned for different videos and objects (say, for example, using training data). One solution for such a problem is to estimate the parameters of the process model while estimating the state. In this paper, we propose such joint estimation of particle filters for object tracking and show that for the same model for state estimation, particle filtering with joint estimation performs better (in terms of the tracking error) than conventional particle filtering.

Original languageEnglish (US)
Title of host publicationIEEE TENCON 2004 - 2004 IEEE Region 10 Conference Proceedings: Analog and Digital Techniques in Electrical Engineering
VolumeA
StatePublished - 2004
EventIEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand
Duration: Nov 21 2004Nov 24 2004

Other

OtherIEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering
CountryThailand
CityChiang Mai
Period11/21/0411/24/04

ASJC Scopus subject areas

  • Engineering(all)

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    Kuchi, P., & Panchanathan, S. (2004). On the use of joint estimation in particle filters for object tracking in video. In IEEE TENCON 2004 - 2004 IEEE Region 10 Conference Proceedings: Analog and Digital Techniques in Electrical Engineering (Vol. A)