Vision-Based Rain Gauge for Dynamic Scenes

Cheen Hau Tan, Jie Chen, Yun Ni, Lap Pui Chau, Ling Min Soh

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In this paper we develop a vision-based rain intensity measurement method for dynamic scenes. The method first measures the area density of rain by analyzing temporal changes in pixel values in the video input. The area density, represented as a binary rain map, is then mapped to a rain intensity value using linear regression. To ensure temporal consistency of scene content across frames in dynamic scenes, we applied superpixel-based content alignment. Potential false detections in the binary rain map are removed using directional morphological opening. Experiments show that both superpixel-based content alignment and morphological opening are important for good rain map generation and rain intensity estimation.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
Publication statusPublished - 31 Jan 2019
Externally publishedYes
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP


Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018


  • Rain estimation
  • rain removal
  • superpixel segmentation

ASJC Scopus subject areas

  • Signal Processing


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