Title :
A robust multi-class traffic sign detection and classification system using asymmetric and symmetric features
Author :
Jiao, Jialin ; Zheng, Zhong ; Park, Jungme ; Murphey, Yi L. ; Luo, Yun
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
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.
Keywords :
image classification; object detection; traffic engineering computing; Adaboost; asymmetric Haar-like features; robust multiclass traffic sign detection; traffic classification system; Cameras; Computer vision; Cybernetics; Layout; Neural networks; Roads; Robustness; Telecommunication traffic; Traffic control; USA Councils; asymmetric features; multi-class classification; traffic sign detection;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346196
