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Hierarchical improvement of foreground segmentation masks in background subtraction

Author
Ortego, Diego; San Miguel Avedillo, Juan Carlosuntranslated; Martínez Sánchez, José Maríauntranslated
Entity
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher
IEEE
Date
2018-06-28
Citation
10.1109/TCSVT.2018.2851440
IEEE Transactions on Circuits and Systems for Video Technology 29.6 (2019): 1645 - 1658
 
 
 
ISSN
1051-8215
DOI
10.1109/TCSVT.2018.2851440
Funded by
This work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R)
Project
Gobierno de España. TEC2014-53176-R
Editor's Version
https://doi.org/10.1109/TCSVT.2018.2851440
Subjects
Foreground segmentation improvement; Background subtraction; Foreground quality; Post-processing; Telecomunicaciones
URI
http://hdl.handle.net/10486/692304
Note
© 2018 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Rights
© 2018 IEEE

Abstract

A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications. In this work, we propose a post-processing framework to improve foreground segmentation performance of background subtraction algorithms. We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground. Firstly, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions. Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions. Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully-connected Conditional Random Field. Experiments are conducted over four large and heterogeneous datasets with varied challenges (CDNET2014, LASIESTA, SABS and BMC) demonstrating the capability of the proposed framework to improve background subtraction results
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  • Producción científica en acceso abierto de la UAM [18125]

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