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Contrast invariant features for human detection in far infrared images

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URI: http://hdl.handle.net/10016/17553
ISSN: 1931-0587
ISBN: 978-1-4673-2119-8 (print)
ISBN: 978-1-4673-2118-1 (online)
DOI: 10.1109/IVS.2012.6232242
UXXI: CC/0000014610
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contrast_IV_2012_ps.pdf (2.02 MB)
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2012
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IEEE
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Abstract
In this paper a new contrast invariant descriptor for human detection in long-wave infrared images is proposed. It exploits local information histogram of orientations of phase coherence. Contrast in infrared images depends on the temperature of the object and the background, which makes gradient based descriptors less robust, especially in daylight conditions. The objective is to obtain a scale, brightness and contrast invariant descriptor that can successfully detect pedestrians in images taken with a cheap, temperature-sensitive, uncooled microbolometer. The descriptor, packed into grids is feed to a Support Vector Machine classifier. The algorithm has been tested in night and day sequences and its performance is compared with a day only descriptor: the histogram of oriented features (HOG).
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Proceeding of: 2012 IEEE Intelligent Vehicles Symposium (IV), Alcalá de Henares, Spain, June 3-7, 2012
Keywords
Bolometers, Infrared images, Object detection, Pattern classification, Pedestrian detection, Support vector machines
Bibliographic citation
2012 IEEE Intelligent Vehicles Symposium (IV), Pp. 117-122
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DISA - LSI - Artículos en Congresos Internacionales