Open Access Open Access  Restricted Access Subscription or Fee Access

Traffic Sign Recognition System

Surabhi Rajeshirke, Payal Patil, Sunny Giri, Gaurav Dhande, Pranjali Kuche

Abstract


 

Sign recognition in the automotive context is a crucial task for Intelligent Transportation Systems. Its objective is to supply the driver with important information found on traffic signs. This information could be speed limits, traffic orders (Stop, for example) or texts that describe the nature of the road ahead. The proposed system is a novel application for the automatic detection and recognition of traffic signs. A video will be given to the system in form of input. The video will be analyzed by the system and the result of detected sign boards will be displayed in text format. The detected text will also be conveyed to the user through verbal message. Thus the output of the proposed system will be in text and audio format.

 


Keywords


SURF, Haar, Descriptor, TSR, Histogram, Octave, Scale

Full Text:

PDF

References


Abdelhamid Mammeri, El-Hebri Khiari and Azzedine Boukerche, “Road-Sign Text Recognition Architecture for Intelligent Transportation Systems”, PARADISE Research Laboratory, DIVA Strategic Research Network University of Ottawa, Ottawa, Ontario, Canada. 2014 IEEE.

Wahyono, Laksono Kurnianggoro, Joko Hariyono, and Kang-Hyun Jo, “Traffic Sign Recognition System for Autonomous Vehicle Using Cascade SVM Classifier”, Graduate School of Electrical Engineering University of Ulsan, 2014.

Jack Greenhalgh and Majid Mirmehdi , “Recognizing Text-Based Traffic Signs”, 2014.

Dajun Ding, Jihwan Yoon Department of Electronic Engineering Soongsil University Seoul, Korea, “Traffic sign detection and identification using SURF algorithm and GPGPU”, 2014.

Herbert Bay, Andreas Ess , Tinne Tuytelaars , and Luc Van Gool, “Speeded-Up Robust Features (SURF)” , ETH Zurich, BIWI Sternwartstrasse 7 CH-8092 Zurich Switzerland.

Long Chen, Qingquan Li, Ming Li and Qingzhou Mao “Traffic Sign Detection and Recognition for Intelligent Vehicle”, 2011 IEEE.

Yan Han, Kushal Virupakshappa and Erdal Oruklu “Robust Traffic Sign Recognition with Feature Extraction and k-NN Classification Methods”,2015 IEEE.

FeiXiang Ren, Jinsheng Huang, Reinhard Klette “General Traffic Sign Recognitionby Feature Matching” 24th International Conference Image and Vision Computing New Zealand (IVCNZ 2009) IEEE.

Li Li School of Business, Sichuan Agricultural University, Sichuan Dujianyan 611830, China “Image Matching Algorithm based on Feature-point and DAISY Descriptor” JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE,2014,IEEE.

Hassan Shojania, “Real-time Traffic Sign Detection” 2014 IEEE.

Dilip Singh Solanki, Dr. Gireesh Dixit, “Traffic Sign Detection and Recognition Using Feature Based and OCR Method” IJRSET.

Toon Goedem´e “TRAFFIC SIGN RECOGNITION” De Nayer Instituut, Jan De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium.

Ryoki Takada and Jiro Katto, “Traffic Sign Recognition by Distorted Template Matching” 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE).

Zhiyong Huang, Yuanlong Yu, Jason Gu, and Huaping Liu “An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine” 2016 IEEE

Hasan Fleyeh “TRAFFIC SIGN RECOGNITION WITHOUT COLOR INFORMATION”Department of Computer Engineering, School of Technology and Business Studies, Dalarna University, Sweden, 2015 IEEE.

Flora Dellinger, Julie Delon, Yann Gousseau, Julien Michel, and Florence Tupin “SAR-SIFT: A SIFT-Like Algorithm for SAR Images” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 53, NO. 1, JANUARY 2015.


Refbacks

  • There are currently no refbacks.