Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30132
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Título : Local boosted features for pedestrian detection
Autor : Villamizar, Michael, Sanfeliu, Alberto, Andrade-Cetto, J.
Fecha de publicación : 2009
Editor: Springer
Citación : Pattern Recognition and Image Analysis: 128-135 (2009)
Citación : Lecture Notes in Computer Science 5524
Resumen: The present paper addresses pedestrian detection using local boosted features that are learned from a small set of training images. Our contribution is to use two boosting steps. The first one learns discriminant local features corresponding to pedestrian parts and the second one selects and combines these boosted features into a robust class classifier. In contrast of other works, our features are based on local differences over Histograms of Oriented Gradients (HoGs). Experiments carried out to a public dataset of pedestrian images show good performance with high classification rates.
Descripción : Trabajo presentado al 4th IbPRIA celebrado en Portugal del 10 al 12 de junio de 2009.
Versión del editor: http://dx.doi.org/10.1007/978-3-642-02172-5_18
URI : http://hdl.handle.net/10261/30132
ISBN : 978-3-642-02171-8
DOI: 10.1007/978-3-642-02172-5_18
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