EGU23-12626
https://doi.org/10.5194/egusphere-egu23-12626
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Real-time Rainfall Estimation Using Binarized Rain Streak Images in Surveillance Cameras

Jongyun Byun1, Jinwook Lee2, Hyeon-Joon Kim3, and Changhyun Jun4
Jongyun Byun et al.
  • 1Chung-Ang University, Department of Civil Engineering, Seoul, Korea, Republic of (whddbs0932@cau.ac.kr)
  • 2Chung-Ang University, Department of Civil Engineering, Seoul, Korea, Republic of (jinwook213@cau.ac.kr)
  • 3Chung-Ang University, Department of Civil Engineering, Seoul, Korea, Republic of (hjkim22@cau.ac.kr)
  • 4Chung-Ang University, Department of Civil Engineering, Seoul, Korea, Republic of (cjun@cau.ac.kr)

Real-time monitoring and analysis of rainfall are important in reducing potential damage and losses in water-related disasters. Nowadays, IoT sensor data is being widely used in weather observation because of cost-effectiveness with high spatiotemporal resolutions. This study proposes a novel approach to estimate rainfall intensity from binarized rain streak images in surveillance cameras. Here, several background subtract algorithms are considered to extract rain streak images from raw video data recorded by surveillance cameras installed in six different points in Seoul, Korea. Various ranges of binarization threshold values are also used to calculate the number of white pixel values from rain streak images. As results, it indicates that rainfall intensity is properly estimated from binarized rain streak images with a relation equation between the number of white values and observation rainfall intensity data, which shows high dependence on the amount of illumination and recording environment characteristics (e.g. rainfall type, camera parameter, etc.).

Keywords: Rainfall Estimation, Rain Streak, CCTV, Computer Vision, Korea

Acknowledgement

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and in part supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

How to cite: Byun, J., Lee, J., Kim, H.-J., and Jun, C.: Real-time Rainfall Estimation Using Binarized Rain Streak Images in Surveillance Cameras, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12626, https://doi.org/10.5194/egusphere-egu23-12626, 2023.