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Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection

  • Rainer Lienhart
  • Alexander Kuranov
  • Vadim Pisarevsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2781)

Abstract

Recently Viola et al. have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce and empirically analysis two extensions to their approach: Firstly, a novel set of rotated haar-like features is introduced. These novel features significantly enrich the simple features of [6] and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. Secondly, we present a through analysis of different boosting algorithms (namely Discrete, Real and Gentle Adaboost) and weak classifiers on the detection performance and computational complexity. We will see that Gentle Adaboost with small CART trees as base classifiers outperform Discrete Adaboost and stumps. The complete object detection training and detection system as well as a trained face detector are available in the Open Computer Vision Library at sourceforge.net [8].

Keywords

False Alarm False Alarm Rate Face Detector Actual Face Weak Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rainer Lienhart
    • 1
  • Alexander Kuranov
    • 1
  • Vadim Pisarevsky
    • 1
  1. 1.Microprocessor Research LabIntel Labs Intel CorporationSanta ClaraUSA

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