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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/30132
Title: Local boosted features for pedestrian detection
Authors: Villamizar, Michael; Sanfeliu, Alberto; Andrade-Cetto, Juan
Issue Date: 2009
Publisher: Springer
Citation: Pattern Recognition and Image Analysis: 128-135 (2009)
Series/Report no.: Lecture Notes in Computer Science 5524
Abstract: 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.
Description: Trabajo presentado al 4th IbPRIA celebrado en Portugal del 10 al 12 de junio de 2009.
Publisher version (URL): http://dx.doi.org/10.1007/978-3-642-02172-5_18
URI: http://hdl.handle.net/10261/30132
DOI: 10.1007/978-3-642-02172-5_18
ISBN: 978-3-642-02171-8
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