Multiple classifier object detection with confidence measures

Authors:
Michael Horton;Mike Cameron-Jones;Raymond Williams
Affiliations:
School of Computing, University of Tasmania, TAS, Australia;School of Computing, University of Tasmania, TAS, Australia;School of Computing, University of Tasmania, TAS, Australia
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

Citing 4
Cited 0

Boosting Chain Learning for Object Detection

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
FloatBoost Learning and Statistical Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Rotational Robustness of Hand Detection with a Viola-Jones Detector

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Detecting Pedestrians Using Patterns of Motion and Appearance

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

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.