Hierarchical improvement of foreground segmentation masks in background subtraction
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
IEEEFecha de edición
2018-06-28Cita
10.1109/TCSVT.2018.2851440
IEEE Transactions on Circuits and Systems for Video Technology 29.6 (2019): 1645 - 1658
ISSN
1051-8215DOI
10.1109/TCSVT.2018.2851440Financiado por
This work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R)Proyecto
Gobierno de España. TEC2014-53176-RVersión del editor
https://doi.org/10.1109/TCSVT.2018.2851440Materias
Foreground segmentation improvement; Background subtraction; Foreground quality; Post-processing; TelecomunicacionesNota
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© 2018 IEEEResumen
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
Lista de ficheros
Google Scholar:Ortego, Diego
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San Miguel Avedillo, Juan Carlos
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Martínez Sánchez, José María
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