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Visual pedestrian recognition in weak classifier space using nonlinear parametric models

Abstract : Pedestrian recognition in images is a challenging task. Indeed a generic model must be able to describe the huge variability of pedestrians. We propose a learning based approach using a training set composed by positive and negative samples. A simple description of each candidate image provides a huge feature vector from which can be built weak classifiers. We select a subset of relevant weak classifiers using a classic AdaBoost algorithm. The resulting subset is then used as binary vectors in a kernel based machine learning classifier (like SVM, RVM, ...). The major contribution of the paper is the original association of an AdaBoost algorithm to select the relevant weak classifiers, followed by a SVM like classifier for which input data are given by the selected weak classifiers. Kernel based machine learning provides non-linear separator into the weak classifier space while standard AdaBoost gives a linear one. Performances of this method are compared to a classical AdaBoost method.
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Conference papers
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https://hal.uca.fr/hal-03099412
Contributor : Christophe Tournayre <>
Submitted on : Wednesday, January 6, 2021 - 10:36:09 AM
Last modification on : Wednesday, April 21, 2021 - 8:34:03 AM

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Laetitia Leyrit, Thierry Chateau, Christophe Tournayre, Jean-Thierry Lapreste. Visual pedestrian recognition in weak classifier space using nonlinear parametric models. 15th IEEE International Conference on Image Processing, Oct 2008, San Diego, United States. pp.2392-2395, ⟨10.1109/ICIP.2008.4712274⟩. ⟨hal-03099412⟩

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