A robust multi-class traffic sign detection and classification system using asymmetric and symmetric features

Authors:
Jialin Jiao;Zhong Zheng;Jungme Park;Yi L. Murphey;Yun Luo
Affiliations:
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI;Robert Bosch LLC., Farmington Hills, MI
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

In this paper we present our research work in traffic sign detection and classification. Specifically we present a set of asymmetric Haar-like features that will be shown to be effective in reducing false alarm rates for traffic sign detection, and a robust multi-class traffic sign detection and classification system built based upon the stage-by-stage performance analysis of individual traffic sign detectors trained using Adaboost.