Vision-based traffic sign compliance evaluation using convolutional neural network
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Document Type
Conference Proceeding
Source Title
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
First Page
120
Last Page
123
Publication Date
6-22-2018
Abstract
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. © 2018 IEEE.
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Digitial Object Identifier (DOI)
10.1109/ICASI.2018.8394502
Recommended Citation
Roxas, E. A., Acilo, J. N., Vicerra, R. P., Dadios, E. P., & Bandala, A. A. (2018). Vision-based traffic sign compliance evaluation using convolutional neural network. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, 120-123. https://doi.org/10.1109/ICASI.2018.8394502
Disciplines
Electrical and Computer Engineering | Electrical and Electronics | Systems and Communications
Keywords
Traffic monitoring; Computer vision; Traffic signs and signals—Control systems
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