Rejection based multipath reconstruction for background estimation in video sequences with stationary objects
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
Elsevier B.V.Fecha de edición
2016-06Cita
10.1016/j.cviu.2016.03.012
Computer Vision and Image Understanding 147 (2016): 23 – 37
ISSN
1077-3142 (print); 1090-235X (online)DOI
10.1016/j.cviu.2016.03.012Financiado por
This work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R) and by the TEC department (Universidad Autónoma de Madrid).Proyecto
Gobierno de España. TEC2014-53176-RVersión del editor
http://dx.doi.org/10.1016/j.cviu.2016.03.012Materias
Background estimation; Background visibility; Clustering; Multipath; Smoothness; Stationary foreground; TelecomunicacionesNota
This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, VOL147 (2016) DOI 10.1016/j.cviu.2016.03.012Derechos
© 2016 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Resumen
Background estimation in video consists in extracting a foreground-free image from a set of training frames. Moving and stationary objects may affect the background visibility, thus invalidating the assumption of many related literature where background is the temporal dominant data. In this paper, we present a temporal-spatial block-level approach for background estimation in video to cope with moving and stationary objects. First, a Temporal Analysis module obtains a compact representation of the training data by motion filtering and dimensionality reduction. Then, a threshold-free hierarchical clustering determines a set of candidates to represent the background for each spatial location (block). Second, a Spatial Analysis module iteratively reconstructs the background using these candidates. For each spatial location, multiple reconstruction hypotheses (paths) are explored to obtain its neighboring locations by enforcing inter-block similarities and intra-block homogeneity constraints in terms of color discontinuity, color dissimilarity and variability. The experimental results show that the proposed approach outperforms the related state-of-the-art over challenging video sequences in presence of moving and stationary objects.
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Google Scholar:Ortego Hernández, Diego
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San Miguel Avedillo, Juan Carlos
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Martínez Sánchez, José María
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