People-background segmentation with unequal error cost
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics EngineersDate
2012Citation
10.1109/ICIP.2012.6466819
19th IEEE International Conference on Image Processing, ICIP 2012. IEEE, 2012. 157-160
ISSN
1522-4880ISBN
978-1-4673-2532-5 (online); 978-1-4673-2534-9 (print)DOI
10.1109/ICIP.2012.6466819Funded by
Work partially supported by the Universidad Autónoma de Madrid (“FPI-UAM”) and by the Spanish Goverment (“TEC2011-25995 EventVideo”). This work was done while the first author was visting Queen Mary University of London.Editor's Version
http://dx.doi.org/10.1109/ICIP.2012.6466819Subjects
Background confidence map; Detection confidence map; People detection; People-background segmentation; TelecomunicacionesNote
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. Á. García-Martín, A. Cavallaro, J. M. Martínez, "People-background segmentation with unequal error cost", in 19th IEEE International Conference on Image Processing, ICIP 2012, p. 157 - 160Rights
© 2012 IEEEAbstract
We address the problem of segmenting a video in two classes of different semantic value, namely background and people, with the goal of guaranteeing that no people (or body parts) are classified as background. Body parts classified as background are given a higher classification error cost (segmentation with bias on background), as opposed to traditional approaches focused on people detection. To generate the people-background segmentation mask, the proposed approach first combines detection confidence maps of body parts and then extends them in order to derive a background mask, which is finally post-processed using morphological operators. Experiments validate the performance of our algorithm in different complex indoor and outdoor scenes with both static and moving cameras.
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Google Scholar:García-Martín, Álvaro
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Cavallaro, Andrea
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Martínez Sánchez, José María
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