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ISPRS Int. J. Geo-Inf. 2019, 8(3), 128; https://doi.org/10.3390/ijgi8030128

Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification

1
Department of Information and Communication Technology, Remote Sensing and GIS, Asian Institute of Technology, Post Box No. 4, Pathumthani 12120, Thailand
2
Department of Sustainable Environmental Engineering, Yamaguchi University, Yamaguchi 755-8611, Japan
3
Center for Spatial Information Science, Tokyo University, Chiba 277-8568, Japan
4
Department of Civil and Infrastructure Engineering, Transportation Engineering, Asian Institute of Technology, Post Box No. 4, Pathumthani 12120, Thailand
5
Department of Industrial Systems Engineering, Microelectronics and Embedded Systems, Asian Institute of Technology, Post Box No. 4, Pathumthani 12120, Thailand
*
Author to whom correspondence should be addressed.
Received: 30 January 2019 / Accepted: 24 February 2019 / Published: 4 March 2019
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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Abstract

Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been deployed recently in various places for surveillance and traffic monitoring. Instead of deploying new sensors designed specifically for monitoring road conditions, images and location information from existing cameras can be used to obtain precise environmental information. Therefore, we propose a road environment extraction framework that covers different situations, such as raining and non-raining scenes, daylight and night-time scenes, crowded and non-crowded traffic, and wet and dry roads. The framework is based on CCTV images from a Bangkok metropolitan dataset, provided by the Bangkok Metropolitan Administration. To obtain information from CCTV image sequences, multi-label classification was considered by applying a convolutional neural network. We also compared various models, including transfer learning techniques, and developed new models in order to obtain optimum results in terms of performance and efficiency. By adding dropout and batch normalization techniques, our model could acceptably perform classification with only a few convolutional layers. Our evaluation showed a Hamming loss and exact match ratio of 0.039 and 0.84, respectively. Finally, a road environment monitoring system was implemented to test the proposed framework. View Full-Text
Keywords: multi-label classification; CCTV; road environment; convolutional neural networks multi-label classification; CCTV; road environment; convolutional neural networks
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Sirirattanapol, C.; NAGAI, M.; Witayangkurn, A.; Pravinvongvuth, S.; Ekpanyapong, M. Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 128.

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