An Efficient Real Time Rectangle Speed Limit Sign Recognition System

被引：1

作者：

Zhang Yankun ^{[1
]}

Hong Chuyang ^{[1
]}

Charles Wang ^{[1
]}

机构：

[1] Harman Internal Natl, Shanghai RD Ctr, Corp Tech Grp, Shanghai 200233, Peoples R China

来源：

2010 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2010年

关键词：

CLASSIFICATION;

D O I：

10.1109/ICOSP.2010.5656721

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper present an efficient real time rectangle speed limit sign recognition system. The system design considers computation load and hardware resources for driver assistant system. First multi-scale overlapping LBP features are used to train AdaBoost cascade classifier for speed limit sign object detection. Then a simple linear prediction method is used to do tracking task. At the recognition stage, a novel efficient algorithm is used to correct rotation angle, and then integral image based adaptive threshold algorithm is adopted to segment the speed limit number. The clustering based binary tree of linear support vector machine is adopted for classification task. The system is tested on real road scene video sequences. It achieves 98.3% recognition rate with approximate 16fps frame rate on laptop.

引用

页码：34 / 38

页数：5

共 50 条

[1] A Real-Time Speed Limit Sign Recognition System for Autonomous Vehicle Using SSD Algorithm
Abu Mangshor, Nur Nabilah
Saharuddin, Nor Syahirah
Ibrahim, Shafaf
Fadzil, Ahmad Firdaus Ahmad
Abu Samah, Khyrina Airin Fariza
[J]. 2021 11TH IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE 2021), 2021, : 126 - 130
[2] Design of real-time speed limit sign recognition and over-speed warning system on mobile device
Chang, Kuei-Chung
Liu, Po-Kai
[J]. 2015 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2015, : 43 - 44
[3] Real-Time Railway Speed Limit Sign Recognition from Video Sequences
Agudo, David
Sanchez, Angel
Velez, Jose F.
Belen Moreno, A.
[J]. PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, (IWSSIP 2016), 2016, : 109 - 112
[4] Real-time Implementation and Design of an Embedded System for Identifying Speed Limit Sign
Su, Ching-Lung
Chen, Kai-Ping
Chen, Bing-Hong
Jeng, Yu-Tong
Chen, Kuan-Hung
[J]. 2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2014,
[5] Efficient Tracking Method to Make a Real Time Sign Language Recognition System
Jebali, Maher
Dalle, Patrice
Jemni, Mohamed
[J]. COMPUTERS HELPING PEOPLE WITH SPECIAL NEEDS, ICCHP 2014, PT II, 2014, 8548 : 454 - 457
[6] An Efficient Real-time Speed Limit Signs Recognition Based on Rotation Invariant Feature
Liu, Wei
Lv, Jin
Gao, Haihua
Duan, Bobo
Yuan, Huai
Zhao, Hong
[J]. 2011 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2011, : 1000 - 1005
[7] A Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System Using GPU Computing
Muyan-Oezcelik, Pinar
Glavtchev, Vladimir
Ota, Jeffrey M.
Owens, John D.
[J]. PATTERN RECOGNITION, 2010, 6376 : 162 - +
[8] A speed limit sign recognition system using artificial neural network
Ishak, Khairul Anuar
Sani, Maizura Mohd
Tahir, Nooritawati Md
Samad, Salina Abdul
Hussain, Aini
[J]. 2006 4TH STUDENT CONFERENCE ON RESEARCH AND DEVELOPMENT, 2006, : 127 - 131
[9] Embedded real-time speed limit sign recognition using image processing and machine learning techniques
Samuel L. Gomes
Elizângela de S. Rebouças
Edson Cavalcanti Neto
João P. Papa
Victor H. C. de Albuquerque
Pedro P. Rebouças Filho
João Manuel R. S. Tavares
[J]. Neural Computing and Applications, 2017, 28 : 573 - 584
[10] Embedded real-time speed limit sign recognition using image processing and machine learning techniques
Gomes, Samuel L.
Reboucas, Elizangela de S.
Neto, Edson Cavalcanti
Papa, Joao P.
de Albuquerque, Victor H. C.
Reboucas Filho, Pedro P.
Tavares, Joao Manuel R. S.
[J]. NEURAL COMPUTING & APPLICATIONS, 2017, 28 : S573 - S584

← 1 2 3 4 5 →