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Sensors 2011, 11(4), 4372-4384; doi:10.3390/s110404372

Boosting-Based On-Road Obstacle Sensing Using Discriminative Weak Classifiers

1
Division of Electronics and Information Engineering, Chonbuk National University, Jeonju 561-756, Korea
2
Department of IT Engineering, Sangmyung University, Chonan 330-720, Korea
*
Author to whom correspondence should be addressed.
Received: 14 January 2011 / Revised: 20 March 2011 / Accepted: 12 April 2011 / Published: 14 April 2011
(This article belongs to the Section Physical Sensors)
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Abstract

This paper proposes an extension of the weak classifiers derived from the Haar-like features for their use in the Viola-Jones object detection system. These weak classifiers differ from the traditional single threshold ones, in that no specific threshold is needed and these classifiers give a more general solution to the non-trivial task of finding thresholds for the Haar-like features. The proposed quadratic discriminant analysis based extension prominently improves the ability of the weak classifiers to discriminate objects and non-objects. The proposed weak classifiers were evaluated by boosting a single stage classifier to detect rear of car. The experiments demonstrate that the object detector based on the proposed weak classifiers yields higher classification performance with less number of weak classifiers than the detector built with traditional single threshold weak classifiers. View Full-Text
Keywords: weak classifiers; Haar-like features; AdaBoost; quadratic discriminant analysis weak classifiers; Haar-like features; AdaBoost; quadratic discriminant analysis
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Adhikari, S.P.; Yoo, H.-J.; Kim, H. Boosting-Based On-Road Obstacle Sensing Using Discriminative Weak Classifiers. Sensors 2011, 11, 4372-4384.

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