Vision-based Traffic Sign Compliance Evaluation using Convolutional Neural Network

被引：0
|
作者：
Roxas, Edison A. [1 ]
Acilo, Joshua N. [1 ]
Vicerra, Ryan Rhay P. [1 ]
Dadios, Elmer P. [1 ]
Bandala, Argel A. [1 ]
机构：
[1] De la Salle Univ, Gokongwei Coll Engn, 2401 Taft Ave, Manila 1004, Philippines
来源：
PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ) | 2018年
关键词：
Computer vision; Traffic sign monitoring and evaluation; Traffic sign compliance and standard; AlexNet;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Manual monitoring of road signs compliance procedures are adapted by developing countries. As effective as this method is, the amount of time and funds needed to cover a large area is quite alarming. Thus, a need for a vision - based traffic sign detection and recognition system. However, while a majority of researches using machine vision focuses on the development of a robust real -time traffic sign recognition system, researches addressing the issue of the sign compliance and standardization is lacking.
引用
收藏
页码：120 / 123
页数：4
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