Water for future (물과 미래)

Korea Water Resources Association (한국수자원학회)
권호 표지

CCTV를 이용한 강우입자분포 및 강우강도 산정

  • 이진욱 (중앙대학교 건설환경플랜트공학과) ;
  • 김현준 (중앙대학교 건설환경플랜트공학과) ;
  • 변종윤 (중앙대학교 건설환경플랜트공학과) ;
  • 백종진 (중앙대학교 건설환경플랜트공학과) ;
  • 전창현 (중앙대학교 건설환경플랜트공학과)
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. Allamano, P., Croci, A., and Laio, F. (2015). Toward the camera rain gauge. Water Resources Research, Vol. 51, No. 3, pp. 1744-1757. https://doi.org/10.1002/2014WR016298
  2. Atlas, D., Srivastava, R. C., and Sekhon, R. S. (1973). Doppler radar characteristics of precipitation at vertical incidence. Reviews of Geophysics, Vol. 11, No. 1, pp. 1-35. https://doi.org/10.1029/RG011i001p00001
  3. Avanzato, R., and Beritelli, F. (2020). A cnn-baseddifferential image processing approach for rainfall classification. Advancesin Science, Technology and Engineering Systems Journal, Vol. 5, No. 4, pp. 438-444. https://doi.org/10.25046/aj050452
  4. Bouwmans, T., El Baf, F., and Vachon, B. (2010). Statistical background modeling for foreground detection: A survey. In Handbook of pattern recognition and computer vision (pp. 181-199).
  5. Duthon, P., Bernardin, F., Chausse, F., and Colomb, M. (2018). Benchmark for the robustness of image features in rainy conditions. Machine Vision and Applications, Vol. 29, No. 5, pp. 915-927. https://doi.org/10.1007/s00138-018-0945-8
  6. Famiglietti, J. S., Cazenave, A., Eicker, A., Reager, J. T., Rodell, M., and Velicogna, I. (2015). Satellites provide the big picture. Science, Vol. 349, No. 6249, pp. 684-685.
  7. Garg, K., and Nayar, S. K. (2005). When does a camera see rain?. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 (Vol. 2, pp. 1067-1074). IEEE.
  8. Garg, K., and Nayar, S. K. (2007). Vision and rain. International Journal of Computer Vision, Vol. 75, No. 1, pp. 3-27. https://doi.org/10.1007/s11263-006-0028-6
  9. Guo, B., Han, Q., Chen, H., Shangguan, L., Zhou, Z., and Yu, Z. (2017). The emergence of visual crowdsensing: Challenges and opportunities. IEEE Communications Surveys & Tutorials, Vol. 19, No. 4, pp. 2526-2543. https://doi.org/10.1109/COMST.2017.2726686
  10. Guo, H., Huang, H., Sun, Y. E., Zhang, Y., Chen, S., and Huang, L. (2019). Chaac: Real-time and fine-grained rain detection and measurement using smartphones. IEEE Internet of Things Journal, Vol. 6, No. 1, pp. 997-1009. https://doi.org/10.1109/jiot.2018.2866690
  11. Haberlandt, U., and Sester, M. (2010). Areal rainfall estimation using moving cars as rain gauges-A modelling study. Hydrology and Earth System Sciences, Vol. 14, No. 7, pp. 1139-1151. https://doi.org/10.5194/hess-14-1139-2010
  12. Hua, X. S. (2018). The city brain: Towards real-time search for the real-world. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 1343-1344). New York: ACM Press.
  13. Jiang, S., Babovic, V., Zheng, Y., and Xiong, J. (2019). Advancing opportunistic sensing in hydrology: A novel approach to measuring rainfall with ordinary surveillance cameras. Water Resources Research, Vol. 55, No. 4, pp. 3004-3027. https://doi.org/10.1029/2018WR024480
  14. Jiang, T. X., Huang, T. Z., Zhao, X. L., Deng, L. J., and Wang, Y. (2018). Fastderain: A novel video rain streak removal method using directional gradient priors. IEEE Transactions on Image Processing, Vol. 28, No. 4, pp. 2089-2102. https://doi.org/10.1109/TIP.2018.2880512
  15. Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., and Kirschbaum, D. B. (2017). So, how much of the Earth's surface is covered by rain gauges?. Bulletin of the American Meteorological Society, Vol. 98, No. 1, pp. 69-78. https://doi.org/10.1175/BAMS-D-14-00283.1
  16. Kim, J. H., Sim, J. Y., and Kim, C. S. (2015). Video deraining and desnowing using temporal correlation and low-rank matrix completion. IEEE Transactions on Image Processing, Vol. 24, No. 9, pp. 2658-2670. https://doi.org/10.1109/TIP.2015.2428933
  17. Loffler-Mang, M., and Joss, J. (2000). An optical disdrometer for measuring size and velocity of hydrometeors. Journal of Atmospheric and Oceanic Technology, Vol. 17, No. 2, pp. 130-139. https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2
  18. McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., ... and Wood, E. F. (2017). The future of Earth observation in hydrology. Hydrology and Earth System Sciences, Vol. 21, No. 7, pp. 3879-3914. https://doi.org/10.5194/hess-21-3879-2017
  19. Morse, J. M. (2005). Evolving trends in qualitative research: Advances in mixed.method design. Qualitative Health Research, Vol. 15, No. 5, pp. 583.585. https://doi.org/10.1177/1049732305275169
  20. Nottle, A., Harborne, D., Braines, D., Alzantot, M., Quintana-Amate, S., Tomsett, R., ... and Preece, A. (2017). Distributed opportunistic sensing and fusion for traffic congestion detection. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1-6). IEEE.
  21. Overeem, A., R. Robinson, J. C., Leijnse, H., Steeneveld, G. J., P. Horn, B. K., and Uijlenhoet, R. (2013). Crowdsourcing urban air temperatures from smartphone battery temperatures. Geophysical Research Letters, Vol. 40, No. 15, pp. 4081-4085. https://doi.org/10.1002/grl.50786
  22. Overeem, A., Leijnse, H., and Uijlenhoet, R. (2016). Two and a half years of country‐wide rainfall maps using radio links from commercial cellular telecommunication networks. Water Resources Research, Vol. 52, No. 10, pp. 8039-8065. https://doi.org/10.1002/2016WR019412
  23. Qasim, S., Khan, K. N., Yu, M., and Khan, M. S. (2021). Performance Evaluation of Background Subtraction Techniques for Video Frames. In 2021 International Conference on Artificial Intelligence (ICAI) (pp. 102-107). IEEE.
  24. Rabiei, E., Haberlandt, U., Sester, M., and Fitzner, D. (2013). Rainfall estimation using moving cars as rain gauges–laboratory experiments. Hydrology and Earth System Sciences, Vol. 17, No. 11, pp. 4701-4712. https://doi.org/10.5194/hess-17-4701-2013
  25. Rabiei, E., Haberlandt, U., Sester, M., Fitzner, D., and Wallner, M. (2016). Areal rainfall estimation using moving cars-Computer experi- ments including hydrological modeling. Hydrology and Earth System Sciences, Vol. 20, No. 9, pp. 3907-3922. https://doi.org/10.5194/hess-20-3907-2016
  26. Santhaseelan, V., and Asari, V. K. (2015). Utilizing local phase information to remove rain from video. International Journal of Computer Vision, Vol. 112, No. 1, pp. 71-89. https://doi.org/10.1007/s11263-014-0759-8
  27. Tripathi, A. K., and Mukhopadhyay, S. (2014). Removal of rain from videos: A review. Signal, Image and Video Processing, Vol. 8, No. 8, pp. 1421-1430. https://doi.org/10.1007/s11760-012-0373-6
  28. Wang, X., Wang, M., Liu, X., Glade, T., Chen, M., Xie, Y., ... and Chen, Y. (2022). Rainfall observation using surveillance audio. Applied Acoustics, Vol. 186, 108478. https://doi.org/10.1016/j.apacoust.2021.108478
  29. Yang, P., and Ng, T. L. (2017). Gauging through the crowd: A crowd‐sourcing approach to urban rainfall measurement and storm water modeling implications. Water Resources Research, Vol. 53, No. 11, pp. 9462-9478. https://doi.org/10.1002/2017WR020682
  30. Zen, R., Arsa, D. M. S., Zhang, R., Er, N. A. S., and Bressan, S. (2019). Rainfall estimation from traffic cameras. In International Conference on Database and Expert Systems Applications (pp. 18-32). Springer, Cham.
  31. Zivkovic, Z., and Van Der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, Vol. 27, No. 7, pp. 773-780. https://doi.org/10.1016/j.patrec.2005.11.005