University of Oulu

Jingjing Xiao, Mourad Oussalah, Robust model adaption for colour-based particle filter tracking with contextual information, Journal of Visual Communication and Image Representation, Volume 79, 2021, 103270, ISSN 1047-3203,

Robust model adaption for colour-based particle filter tracking with contextual information

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Author: Xiao, Jingjing1,2; Oussalah, Mourad3
Organizations: 1University of Birmingham, School of Engineering, Birmingham, UK
2Department of Medical Engineering, Xinqiao Hospital, Chongqing, China
3University of Oulu, Faculty of Information Technology and Electrical Engineering, CMVS, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7 MB)
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Language: English
Published: Elsevier, 2021
Publish Date: 2021-10-04


Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.

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Series: Journal of visual communication and image representation
ISSN: 1047-3203
ISSN-E: 1095-9076
ISSN-L: 1047-3203
Volume: 79
Article number: 103270
DOI: 10.1016/j.jvcir.2021.103270
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The first author would like to thank China of Academy of Science for Financial Support during his stay at University of Birmingham. The second author also thanks University of Oulu and the Academy of Finland Profi5 project #326291 for supporting thesis work that helped perform some programming tasks in this paper.
Academy of Finland Grant Number: 326291
Detailed Information: 326291 (Academy of Finland Funding decision)
Copyright information: © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (