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.
Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing