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Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks

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Abstract

The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this article, a system based on computer vision is presented which detects the presence of rain or snow. To separate the foreground from the background in image sequences, a classical Gaussian Mixture Model is used. The foreground model serves to detect rain and snow, since these are dynamic weather phenomena. Selection rules based on photometry and size are proposed in order to select the potential rain streaks. Then a Histogram of Orientations of rain or snow Streaks (HOS), estimated with the method of geometric moments, is computed, which is assumed to follow a model of Gaussian-uniform mixture. The Gaussian distribution represents the orientation of the rain or the snow whereas the uniform distribution represents the orientation of the noise. An algorithm of expectation maximization is used to separate these two distributions. Following a goodness-of-fit test, the Gaussian distribution is temporally smoothed and its amplitude allows deciding the presence of rain or snow. When the presence of rain or of snow is detected, the HOS makes it possible to detect the pixels of rain or of snow in the foreground images, and to estimate the intensity of the precipitation of rain or of snow. The applications of the method are numerous and include the detection of critical weather conditions, the observation of weather, the reliability improvement of video-surveillance systems and rain rendering.

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References

  • Barnum, P., Narasimhan, S., & Kanade, T. (2010). Analysis of rain and snow in frequency space. International Journal of Computer Vision, 86(2–3), 256–274.

    Article  Google Scholar 

  • Brewer, N., & Liu, N. (2008). Using the shape characteristics of rain to identify and remove rain from video. In Lecture notes in computer science: Vol. 5342. Joint IAPR international workshop SSPR & SPR, Orlando, USA (pp. 451–458). Berlin: Springer.

    Google Scholar 

  • Brignolo, R., Andreone, L., & Burzio, G. (2006). The SAFESPOT Integrated Project: Co-operative systems for road safety. In Transport research arena.

    Google Scholar 

  • Carsten, O. M. J., & Tate, F. N. (2005). Intelligent speed adaptation: accident savings and cost-benefit analysis. Accident Analysis and Prevention, 37, 407–416.

    Article  Google Scholar 

  • Cheung, S. C., & Kamath, C. (2004). Robust techniques for background substraction in urban traffic video. In Video communications and image processing, SPIE Electroning Imaging (pp. 881–892).

    Google Scholar 

  • Dallal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 886–893).

    Google Scholar 

  • Dean, N., & Raftery, A. (2005). Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics, 6(173), 1–14.

    Google Scholar 

  • Deriche, R. (1987). Using Canny’s criteria to derive an optimal edge detector recursively implemented. International Journal of Computer Vision, 2(1), 167–187.

    Article  Google Scholar 

  • Duda, R. O., & Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15, 11–15.

    Article  Google Scholar 

  • Edwards, J. B. (1998). The relationship between road accident severity and recorded weather. Journal of Safety Research, 29(4), 249–262.

    Article  Google Scholar 

  • Elgammal, A., Duraiswami, R., Harwood, D., & Davis, L. S. (2002). Background and foreground modeling using non-parametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 90(7), 1151–1163.

    Article  Google Scholar 

  • Gallen, R., Hautière, N., & Glaser, S. (2010). Advisory speed for intelligent speed adaptation in adverse conditions. In IEEE intelligent vehicles symposium (pp. 107–114).

    Google Scholar 

  • Garg, K., & Nayar, S. (2006). Photorealistic rendering of rain streaks. ACM Transactions on Graphics, 25(3), 996–1002.

    Article  Google Scholar 

  • Garg, K., & Nayar, S. (2007). Vision and rain. International Journal of Computer Vision, 75(1), 3–27.

    Article  Google Scholar 

  • Gruyer, D., Royère, C., Blosseville, J.-M., Michel, G., & Du Lac, N. (2006). SiVIC and RTMaps, interconnected platforms for the conception and the evaluation of driving assistance systems. In ITS world congress.

    Google Scholar 

  • Halimeh, J., & Roser, M. (2009). Raindrop detection on car windshield using geometric-photometric environment construction and intensity-based correlation. In IEEE intelligent vehicles symposium (pp. 610–615).

    Google Scholar 

  • Hase, H., Miyake, K., & Yoneda, M. (1999). Real-time snowfall noise elimination. In IEEE international conference on image processing (Vol. 2, pp. 406–409).

    Google Scholar 

  • Hauser, D., Amayenc, P., Nutten, B., & Waldteufel, P. (1984). A new optical instrument for simultaneous measurement of raindrop diameter and fall speed distributions. Journal of Atmospheric and Oceanic Technology, 1, 256–269.

    Article  Google Scholar 

  • Hautière, N., Tarel, J.-P., Lavenant, J., & Aubert, D. (2006). Automatic fog detection and estimation of the visibility distance through use of an onboard camera. Machine Vision Applications, 17(1), 8–20.

    Article  Google Scholar 

  • Hautière, N., Bossu, J., Bigorgne, E., Hiblot, N., Boubezoul, A., Lusetti, B., & Aubert, D. (2009). Sensing the visibility range at low cost in the SAFESPOT road-side unit. In ITS world congress.

    Google Scholar 

  • He, K., Sun, J., & Tang, X. (2009). Single image haze removal using dark channel prior. In IEEE conference on computer vision and pattern recognition (pp. 1956–1963).

    Google Scholar 

  • Jacobs, N., Burgin, W., Fridrich, N., Abrams, A., Miskell, K., Braswell, B., Richardson, A., & Pless, R. (2009). The global network of outdoor webcams: Properties and applications. In ACM international conference on advances in geographic information systems.

    Google Scholar 

  • Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Transactions of the ASME Journal of Basic Engineering, 83, 95–107.

    MathSciNet  Google Scholar 

  • Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine Learning, 30, 271–274.

    Article  Google Scholar 

  • Kurata, R., Watanabe, H., Tohno, M., Ishii, T., & Oouchi, H. (2004). Evaluation of the detection characteristics of road sensors under poor-visibility conditions. In IEEE intelligent vehicles symposium (pp. 538–543).

    Chapter  Google Scholar 

  • Middleton, W. E. K. (1969). Invention of the meteorological instruments. Baltimore: John Hopkins Press.

    Google Scholar 

  • Mittal, A., Monnet, A., & Paragios, N. (2009). Scene modeling and change detection in dynamic scenes: a subspace approach. Computer Vision and Image Understanding, 113(1), 63–79.

    Article  Google Scholar 

  • Narasimhan, S. G., & Nayar, S. K. (2002). Vision and the atmosphere. International Journal of Computer Vision, 48(3), 233–254.

    Article  MATH  Google Scholar 

  • Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33, 1065–1076.

    Article  MATH  MathSciNet  Google Scholar 

  • Safee-Rad, R., Smith, K. C., Benhabib, B., & Tchoukanov, I. (1992). Application of moment and Fourier descriptors to the accurate estimation of elliptical-shape parameters. Pattern Recognition Letters, 13, 497–508.

    Article  Google Scholar 

  • Sakaino, H., Shen, Y., Pang, Y., & Ma, L. (2009). Falling snow motion estimation based on a semi-transparent and particle trajectory model. In IEEE international conference on image processing (pp. 1609–1612).

    Google Scholar 

  • Stauffer, C., & Grimson, W. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 747–757.

    Article  Google Scholar 

  • Swets, J. (1988). Measuring the accuracy of diagnostic system. Science, 240(4857), 1285–1293.

    Article  MathSciNet  Google Scholar 

  • Tan, R. T. (2008). Visibility in bad weather from a single image. In IEEE conference on computer vision and pattern recognition.

    Google Scholar 

  • Tarel, J.-P., & Hautière, N. (2009). Fast visibility restoration from a single color or gray level image. In IEEE international conference on computer vision (pp. 2201–2208).

    Chapter  Google Scholar 

  • Tavakkoli, A., Nicolescu, M., Bebis, G., & Nicolescu, M. (2009). Non-parametric statistical background modeling for efficient foreground region detection. Machine Vision and Applications, 20(6), 395–409.

    Article  Google Scholar 

  • Tisse, C., Nguyen, H., Tessières, R., Pyanet, M., & Guichard, F. (2008). Extended depth-of-field (edof) using sharpness transport across colour channels. In SPIE conference (Vol. 7061).

    Google Scholar 

  • Wixson, L., Hanna, K., & Mishra, D. (1998). Improved illumination assessment for vision-based traffic monitoring. In IEEE international workshop on visual surveillance (Vol. 2, pp. 34–41).

    Google Scholar 

  • Zhang, X., Li, H., Qi, Y., Kheng, W., & Khim, T. (2006). Rain removal in video by combining temporal and chromatic properties. In IEEE international conference on multimedia and expo (pp. 461–464).

    Chapter  Google Scholar 

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Correspondence to Nicolas Hautière.

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Bossu, J., Hautière, N. & Tarel, JP. Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks. Int J Comput Vis 93, 348–367 (2011). https://doi.org/10.1007/s11263-011-0421-7

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  • DOI: https://doi.org/10.1007/s11263-011-0421-7

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