DocumentCode
2569684
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
fYear
2009
fDate
11-14 Oct. 2009
Firstpage
3421
Lastpage
3427
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location
San Antonio, TX
ISSN
1062-922X
Print_ISBN
978-1-4244-2793-2
Electronic_ISBN
1062-922X
Type
conf
DOI
10.1109/ICSMC.2009.5346196
Filename
5346196
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