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Profile Hidden Markov Models for Foreground Object Modelling

Citation Author(s):
Francisco Florez-Revuelta, Jean-Christophe Nebel
Submitted by:
Ioannis Kazantzidis
Last updated:
12 October 2018 - 7:02am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Ioannis Kazantzidis
Paper Code:
1970

Abstract 

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.

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Dataset Files

Bioinformatics-inspired video analysis

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