Multiple Classifier Object Detection with Confidence Measures

Horton, Michael and Cameron-Jones, Mike and Williams, Raymond (2007) Multiple Classifier Object Detection with Confidence Measures. AI 2007: Advances in Artificial Intelligence, 1 . pp. 559-568. ISSN 1611-3349

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Official URL: http://dx.doi.org/10.1007/978-3-540-76928-6

Abstract

This paper describes an extension to the Haar Classifier Cascade technique for object detection. Existing Haar Classifier Cascades are binary; the extension adds confidence measurement. This confidence measure was implemented and found to improve accuracy on two object detection problems: face detection and fish detection. For fish detection, the problem of selecting positive training-sample angle-ranges was also considered; results showed that large random variations that result in cascades covering overlapping ranges increases their accuracy.

Item Type:	Article
Additional Information:	After this paper was submitted for publication, errors were found in the positive sample generation and in the OpenCV training algorithms. These errors made the results in section 4.2 invalid. This version of the paper contains the correct results, which differ from those in the conference proceedings. The original publication is available at www.springerlink.com
Keywords:	Object detection,Fish detection,Haar Classifier Cascades
Subjects:	280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280203 Image Processing 280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280208 Computer Vision 280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280207 Pattern Recognition
ID Code:	2694
Deposited By:	Mr Michael Horton
Deposited On:	14 Dec 2007 14:47
Last Modified:	09 Jan 2008 02:30
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Multiple Classifier Object Detection with Confidence Measures. (deposited 21 Dec 2007 10:40)
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