[1]ZHANG Yan-kun,HONG Chu-yang,WANG Charles.A real time rectangular speed limit sign recognition system[J].CAAI Transactions on Intelligent Systems,2010,5(6):540-544.
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A real time rectangular speed limit sign recognition system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 6 Page number: 540-544 Column: 学术论文—智能系统 Public date: 2010-12-25
Title:
A real time rectangular speed limit sign recognition system
Author(s):
ZHANG Yan-kun;  HONG Chu-yang;  WANG Charles
Harman International, Shanghai RD Center Corporate Technology Group, Shanghai 200233, China
Keywords:
speed limit sign;  driver assistant system;  local binary pattern;  PCA;  binary tree;  support vector machine
CLC:
TP273
DOI:
-
Abstract:
Speed limit sign recognition is one of the important components for a driver assistance system. An efficient real time rectangular speed limit sign recognition system was proposed. The system framework design considered the computation load and hardware resources for a driver assistance system. First, multiscale overlapping local binary pattern (LBP) image features were used to train an AdaBoost cascade classifier for sign detection. Then a simple linear prediction method was used to do the tracking task. In the recognition stage, the projection method was used to correct the rotation angle and then the integral image based adaptive threshold algorithm was applied to segment the speed limit number, and then the principal component analysis (PCA) was used for feature vector extraction. Finally, a clustering based binary tree of a linear support vector machine was designed for the classification task. The system achieved a 98.3% recognition rate with an approximate frame rate of 16fps in video files for the laptop computer system during actual road tests.
References:
［1］BARGETON A, MOUTARDE F, NASHASHIBI F, BRADAI B. Improving panEuropean speedlimit signs recognition with a new “global number segmentation before digit recognition［C］//IEEE Intelligent Vehicles Symposium. Eindhoven, Netherlands, 2008: 349354.
［2］KELLER C G, SPRUNK C, BAHLMANN C, GIEBEL J, BARATOFF G. Realtime recognition of US speed signs［C］//IEEE Intelligent Vehicles Symposium. Eindhoven, Netherlands, 2008： 518523.
 ［3］LOY G, BARNES N. Fast shapebased road sign detection for a driver assistance system［C］//IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, Japan, 2004: 7075.
［4］BAHLMANN C, ZHU Ying, RAMESH V, PELLKOFER M, KOEHLER T. A system for traffic sign detection, tracking, and recognition using color, shape, and motion information［C］//IIEEE Intelligent Vehicles Symposium. Las Vegas, USA, 2005: 255260.
［5］VIOLA P, JONES M. Robust realtime object detection［J］. International Journal of Computer Vision, 2004, 57(2): 137154.
［6］陈春燕, 章品正, 罗立民. 基于粒特征和连续Adaboost的人脸检测［J］. 智能系统学报， 2009， 4(5): 446452.
CHEN Chunyan, ZHANG Pinzheng, LUO Limin. Face detection using real Adaboost on granular features［J］. CAAI Transactions on Intelligent Systems, 2009, 4(5): 446452.
［7］OJALA T, PIETIK〖AKA¨〗INEN M, M&ENP T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971987.
［8］ZHANG Lun, CHU Rufeng, XIANG Shiming, LI S Z. Face detection based on multiblock LBP representation［C］//Proceedings of IAPR/IEEE International Conference on Biometrics. Seoul, Korea, 2007: 1118.
［9］BRADLEY G, ROTH G. Adaptive thresholding using integral image［J］. Journal of Graphics Tools, 2007, 12(2): 1321.
［10］FEI B, LIU J. Binary tree of SVM: a new fast multiclass training and classification algorithm［J］. IEEE Trans on Neural Networks, 2006, 17(3): 696704.
［11］张铭钧, 尚云超, 杨杰. 基于灰度梯度不变矩的水下目标识别系统［J］.哈尔滨工程大学学报， 2009, 6： 653657. 
ZHANG Mingjun, SHANG Yunchao, YANG Jie. Recognition system for underwater objects based on gray and grads invariant moments［J］.Journal of Harbin Engineering University, 2009, 6： 653657.
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