Real-time embedded system for traffic sign recognition based on ZedBoard

被引:0
|
作者
Wajdi Farhat
Hassene Faiedh
Chokri Souani
Kamel Besbes
机构
[1] Monastir University,Laboratory of Microelectronics and Instrumentation
[2] Sousse University,National School of Engineers
[3] Sousse University,Higher Institute of Applied Sciences and Technology
[4] Sousse University,Center for Research on Microelectronics and Nanotechnology of Sousse
来源
关键词
ADAS; Detection; FPGA; Image processing; Real-time; Recognition; Video;
D O I
暂无
中图分类号
学科分类号
摘要
This paper presents a design methodology of a real-time embedded system that processes the detection and recognition of road signs while the vehicle is moving. An efficient algorithm was proposed, which operates in two processing steps: the detection and the recognition. Regions of interest were extracted by using the Maximally Stable Extremal Regions Method. For the recognition phase, Oriented FAST and Rotated BRIEF features were used. A hardware system based on the Xilinx Zynq platform was developed. The designed system can achieve real-time video processing while assuring constraints and a high-level accuracy in terms of detection and recognition rates.
引用
收藏
页码:1813 / 1823
页数:10
相关论文
共 50 条
  • [1] Real-time embedded system for traffic sign recognition based on ZedBoard
    Farhat, Wajdi
    Faiedh, Hassene
    Souani, Chokri
    Besbes, Kamel
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2019, 16 (05) : 1813 - 1823
  • [2] Embedded Real-Time System for Traffic Sign Recognition on ARM Processor
    Faiedh, Hassene
    Farhat, Wajdi
    Hamdi, Sabrine
    Souani, Chokri
    INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING, 2020, 11 (02) : 77 - 98
  • [3] A GPU-Based Real-Time Traffic Sign Detection and Recognition System
    Chen, Zhilu
    Huang, Xinming
    Ni, Zhen
    He, Haibo
    2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN VEHICLES AND TRANSPORTATION SYSTEMS (CIVTS), 2014, : 1 - 5
  • [4] Active vision system for real-time traffic sign recognition
    Miura, Jun
    Kanda, Tsuyoshi
    Shirai, Yoshiaki
    2000, IEEE, Piscataway, NJ, United States
  • [5] An active vision system for real-time traffic sign recognition
    Miura, J
    Kanda, T
    Shirai, Y
    2000 IEEE INTELLIGENT TRANSPORTATION SYSTEMS PROCEEDINGS, 2000, : 52 - 57
  • [6] Real-Time Embedded Traffic Sign Recognition Using Efficient Convolutional Neural Network
    Xie Bangquan
    Xiong, Weng Xiao
    IEEE ACCESS, 2019, 7 : 53330 - 53346
  • [7] Real-Time Traffic Sign Recognition Based on Shape and Color Classification
    Caglayan, Tughan
    Ahmadzay, Habibullah
    Kofraz, Gokhan
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1897 - 1900
  • [8] Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild
    Li, Jia
    Wang, Zengfu
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (03) : 975 - 984
  • [9] CNN Design for Real-Time Traffic Sign Recognition
    Shustanov, Alexander
    Yakimov, Pavel
    3RD INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGY AND NANOTECHNOLOGY (ITNT-2017), 2017, 201 : 718 - 725
  • [10] Real-Time Traffic Sign Detection and Recognition on FPGA
    Yalcin, Huseyin
    Irmak, Hasan
    Bulut, Mehmet Mete
    Akar, Gozde Bozdagi
    2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,