Bhaskar, H., Mihaylova, L. and Maskell, S. (2013) Articulated human body parts detection based on cluster background subtraction and foreground matching. Neurocomputing, 100. 58 - 73. ISSN 0925-2312
Abstract
Detecting people or other articulated objects and localising their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive cluster background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground matching with rectangular pictorial structures. The efficiency of the designed human body parts tracking framework is illustrated over various real-world video sequences.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 Elsevier. This is an author produced version of a paper subsequently published in Neurocomputing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Human target tracking; Background subtraction; Optimisation; Genetic algorithm; Pictorial structures |
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: | 01 Dec 2014 15:47 |
Last Modified: | 29 Mar 2018 18:31 |
Published Version: | http://dx.doi.org/10.1016/j.neucom.2011.12.039 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | No |
Identification Number: | 10.1016/j.neucom.2011.12.039 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82266 |