2 research outputs found
Hierarchical improvement of foreground segmentation masks in background subtraction
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 resultsThis work was partially supported by the Spanish Government
(HAVideo, TEC2014-53176-R
Quality-Driven video analysis for the improvement of foreground segmentation
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones.Fecha de lectura: 15-06-2018It was partially supported by the Spanish
Government (TEC2014-53176-R, HAVideo