Visual Weather Temperature Prediction

Wei Ta Chu, Kai Chia Ho, Ali Borji

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating temperature of a single outdoor image, and b) predicting temperature of the last image in an image sequence. In the first scenario, visual features are extracted by a convolutional neural network trained on a large-scale image dataset. We demonstrate that promising performance can be obtained, and analyze how volume of training data influences performance. In the second scenario, we consider the temporal evolution of visual appearance, and construct a recurrent neural network to predict the temperature of the last image in a given image sequence. We obtain better prediction accuracy compared to the state-of-the-art models. Further, we investigate how performance varies when information is extracted from different scene regions, and when images are captured in different daytime hours. Our approach further reinforces the idea of using only visual information for cost efficient weather prediction in the future.

原文English
主出版物標題Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面234-241
頁數8
ISBN(電子)9781538648865
DOIs
出版狀態Published - 2018 五月 3
事件18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States
持續時間: 2018 三月 122018 三月 15

出版系列

名字Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
2018-January

Conference

Conference18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
國家United States
城市Lake Tahoe
期間18-03-1218-03-15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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