Post-processing approaches for improving people detection performance
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Elsevier B.V.Date
2015-04Citation
10.1016/j.cviu.2014.09.010
Computer Vision and Image Understanding 133 (2015): 76-89
ISSN
1077-3142 (print); 1090-235X (online)DOI
10.1016/j.cviu.2014.09.010Funded by
This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo).Editor's Version
http://dx.doi.org/10.1016/j.cviu.2014.09.010Subjects
Decision-level fusion; Fusion methods; People detection; People-background segmentation; Segmentation confidence map; Segmentation mask; TelecomunicacionesNote
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, 133 (2015) DOI: 10.1016/j.cviu.2014.09.010Rights
© 2015 Elsevier B.V. All rights reservedEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques.
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Google Scholar:García-Martín, Álvaro
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Martínez Sánchez, José María
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