Cited By
View all- You YLu CWang WTang C(2019)Relative CNN-RNNIEEE Transactions on Image Processing10.1109/TIP.2018.285721928:1(45-55)Online publication date: 1-Jan-2019
Estimating meteorological visibility using cameras: a probabilistic model-driven approach
Authors: 
Nicolas Hautiére, Raouf Babari, Éric Dumont, 
Roland Brémond, Nicolas PaparoditisAuthors Info & Claims
Pages 243 - 254
Abstract
Estimating the atmospheric or meteorological visibility distance is very important for air and ground transport safety, as well as for air quality. However, there is no holistic approach to tackle the problem by camera. Most existing methods are data-driven approaches, which perform a linear regression between the contrast in the scene and the visual range estimated by means of reference additional sensors. In this paper, we propose a probabilistic model-based approach which takes into account the distribution of contrasts in the scene. It is robust to illumination variations in the scene by taking into account the Lambertian surfaces. To evaluate our model, meteorological ground truth data were collected, showing very promising results. This works opens new perspectives in the computer vision community dealing with environmental issues.
References
[1]
Jacobs, N., Burgin, W., Fridrich, N., Abrams, A., Miskell, K., Braswell, B., Richardson, A., Pless, R.: The global network of outdoor webcams: Properties and aplications. In: ACM International Conference on Advances in Geographic Information Systems, ACM GIS 2009 (2009).
[2]
Bush, C., Debes, E.:Wavelet transform for analyzing fog visibility. IEEE Intelligent Systems 13(6), 66-71 (1998).
[3]
Hautière, N., Bigorgne, E., Bossu, J., Aubert, D.: Meteorological conditions processing for vision-based traffic monitoring. In: International Workshop on Visual Surveillance, European Conference on Computer Vision (2008).
[4]
Bäumer, D., Versick, S., Vogel, B.: Determination of the visibility using a digital panorama camera. Atmospheric Environment 42, 2593-2602 (2008).
[5]
Hallowell, R., Matthews, M., Pisano, P.: An automated visibility detection algorithm utilizing camera imagery. In: 23rd Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology (IIPS), San Antonio, TX, Amer. Meteor. Soc (2007).
[6]
Liaw, J.J., Lian, S.B., Chen, R.C.: Atmospheric visibility monitoring using digital image analysis techniques. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1204-1211. Springer, Heidelberg (2009).
[7]
Hagiwara, T., Ota, Y., Kaneda, Y., Nagata, Y., Araki, K.: A method of processing CCTV digital images for poor visibility identification. Transportation Research Records 1973, 95-104 (2007).
[8]
Xie, L., Chiu, A., Newsam, S.: Estimating atmospheric visibility using generalpurpose cameras. In: Bebis, G. (ed.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 356-367. Springer, Heidelberg (2008).
[9]
Luo, C.H., Wen, C.Y., Yuan, C.S., Liaw, J.-L., Lo, C.C., Chiu, S.H.: Investigation of urban atmospheric visibility by high-frequency extraction: Model development and field test. Atmospheric Environment 39, 2545-2552 (2005).
[10]
Middleton, W.: Vision through the atmosphere. University of Toronto Press (1952).
[11]
CIE: International Lighting Vocabulary. Number 17.4 (1987).
[12]
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233-254 (2002).
[13]
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(6), 713-724 (2003).
[14]
Hautiére, N., Tarel, J.P., Aubert, D.: Towards fog-free in-vehicle vision systems through contrast restoration. In: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA (2007).
[15]
Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (2008).
[16]
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA (2009).
[17]
Tarel, J.P., Hautiére, N.: Fast visibility restoration from a single color or gray level image. In: IEEE International Conference on Computer Vision, Kyoto, Japan (2009).
[18]
Corless, R.M., Gonnet, G.H., Hare, D.E.G., Jeffrey, D.J., Knuth, D.E.: On the Lambert W function. Advances in Computational Mathematics 5, 329-359 (1996).
- Estimating meteorological visibility using cameras: a probabilistic model-driven approach
Recommendations
Multiview Panoramic Cameras Using Mirror Pyramids
A mirror pyramid consists of a set of planar mirror faces arranged around an axis of symmetry and inclined to form a pyramid. By strategically positioning a number of conventional cameras around a mirror pyramid, the viewpoints of the cameras' mirror ...
Stereo vision using two PTZ cameras
The research of traditional stereo vision is mainly based on static cameras. As PTZ (Pan-Tilt-Zoom) cameras are able to obtain multi-view-angle and multi-resolution information, they have received more and more concern in both research and real ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Sponsors
- NEXTWINDOW: NextWindow - Touch-Screen Technology
- ADEPT: Adept Electronic Solutions
- AFCV: The Asian Federation of Computer Vision Societies
- NICTA: National Information and Communications Technology Australia
- 4DVIEWS: 4D View Solutions
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 08 November 2010
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
Cited By
View all- You YLu CWang WTang C(2019)Relative CNN-RNNIEEE Transactions on Image Processing10.1109/TIP.2018.285721928:1(45-55)Online publication date: 1-Jan-2019