Application of convolutional neural networks for visibility estimation of CCTV images
- Authors
- Giyenko, A.; Palvanov, A.; Cho, Y.
- Issue Date
- 2018
- Publisher
- IEEE Computer Society
- Keywords
- Atmospheric visibility; Convolutional neural lietivorks; Deep learning; Machine learning; Neural networks; Smart city
- Citation
- International Conference on Information Networking, v.2018-January, pp.875 - 879
- Journal Title
- International Conference on Information Networking
- Volume
- 2018-January
- Start Page
- 875
- End Page
- 879
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4355
- DOI
- 10.1109/ICOIN.2018.8343247
- ISSN
- 1976-7684
- 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. © 2018 IEEE.
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