Kazantzidis, Ioannis, Florez Revuelta, Francisco and Nebel, Jean-Christophe (2018) Profile hidden Markov models for foreground object modelling. In: 25th IEEE International Conference on Image Processing (ICIP); 07 Oct - 10 Oct 2018, Athens, Greece.

Abstract

Accurate background/foreground segmentation is a preliminary process essential to most visual surveillance applications. With the increasing use of freely moving cameras, strategies have been proposed to refine initial segmentation. In this paper, it is proposed to exploit the Vide-omics paradigm, and Profile Hidden Markov Models in particular, to create a new type of object descriptors relying on spatiotemporal information. Performance of the proposed methodology has been evaluated using a standard dataset of videos captured by moving cameras. Results show that usage of the proposed object descriptors allows better foreground extraction than standard approaches.

Downloads Metadata

Full text available as:

Official URL:	http://dx.doi.org/10.1109/ICIP.2018.8451476
Item Type:	Conference or Workshop Item (Paper)
Event Title:	25th IEEE International Conference on Image Processing (ICIP)
Organising Body:	IEEE
Additional Information:	Published in: 2018 25th IEEE International Conference on Image Processing (ICIP). Piscataway, U.S. : Institute of Electrical and Electronics Engineers, Inc. ISSN (online) 2381-8549 ISBN 9781479970629
Research Area:	Research Areas > Computer science and informatics
Faculty, School or Research Centre:	Faculty of Science, Engineering and Computing Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Date Deposited:	06 Apr 2020 13:58
Last Modified:	06 Oct 2020 15:33
DOI:	https://doi.org/10.1109/ICIP.2018.8451476
URI:	https://eprints.kingston.ac.uk/id/eprint/42430

Actions (Repository Editors)

Item Control Page