Bhaskar, H. and Mihaylova, L. (2011) Combined Data Association and Evolving Particle Filter for Tracking of Multiple Articulated Objects. EURASIP Journal on Image and Video Processing, 2011. 642532 . ISSN 1687-5176
Abstract
This paper proposes an approach for tracking multiple articulated targets using a combined data association and evolving population particle filter. A visual target is represented as a pictorial structure using a collection of parts together with a model of their geometry. Tracking multiple targets in video involves an iterative alternating scheme of selecting valid measurements belonging to a target from a clutter or other measurements that all fall within a validation gate. An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed. Variety in the particles is introduced using constrained genetic operators both in the sampling and resampling steps. We explore the effect of various model parameters on system performance and show that the proposed model achieves better accuracy than other widely used methods on standard datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: |
© 2011 Harish Bhaskar and Lyudmila Mihaylova. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Dec 2014 10:21 |
Last Modified: | 23 Jun 2023 21:43 |
Published Version: | http://dx.doi.org/10.1155/2011/642532 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1155/2011/642532 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82276 |