| Title |
Application of Convolutional Neural Networks for visibility estimation of CCTV images |
| ID_Doc |
38969 |
| Authors |
Giyenko, A; Palvanov, A; Cho, Y |
| Title |
Application of Convolutional Neural Networks for visibility estimation of CCTV images |
| Year |
2018 |
| Published |
|
| DOI |
|
| Abstract |
In this paper we discuss the possibility of application of a Convolutional Neural Network for visual atmospheric visibility estimation. A system utilizing such a neural network can greatly benefit a smart city by providing real time localized visibility data across all highways and roads by utilizing a dense network of traffic and security cameras that exist in most developed urban areas. To achieve this, we implemented a Convolutional Neural Network with 3 convolution layers and trained it on a data set taken from CCTV cameras in South Korea. This approach allowed us achieve accuracy above 84%. In the paper we describe the network structure and training process, as well as some final thoughts on the next steps in our research. |
| Author Keywords |
neural networks; deep learning; machine learning; convolutional neural networks; smart city; atmospheric visibility |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000468812000174 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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