Posterior Probability Based Multi-classifier Fusion in Pedestrian Detection

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 238)

Abstract

This paper presents a novel method for pedestrian detection at measurement level. At feature extraction stage, we use Histogram of Oriented Gradient to describe the feature of pedestrian and non-pedestrian. To decrease the time cost, we reduce the dimension by using PCA. The base classifiers used in posterior probability based multi-classifier fusion are posterior probability based SVM, Naïve Bayesian and Minimum Distance Classifier, respectively. To estimate the accuracy of fusion result, stratified cross-validation is used. Experimental results on pedestrian databases prove the efficiency of this work.

Keywords

pedestrian detection multi-classifier fusion posterior probability stratified cross-validation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina

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