A robust multi-class traffic sign detection and classification system using asymmetric and symmetric features
Jia-lin Jiao, Zhong Zheng, Jungme Park, Yi Lu Murphey, Yun Luo · 2009
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.