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DAGM
2003
Springer

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

9 years 4 months ago
Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection
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].
Rainer Lienhart, Alexander Kuranov, Vadim Pisarevs
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where DAGM
Authors Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky
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