@inproceedings{75680c904b24473ea317a0a50eb93c64,
title = "Vision-Based Rain Gauge for Dynamic Scenes",
abstract = "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.",
keywords = "Rain estimation, rain removal, superpixel segmentation",
author = "Tan, {Cheen Hau} and Jie Chen and Yun Ni and Chau, {Lap Pui} and Soh, {Ling Min}",
note = "Funding Information: The research was partially supported by the ST Engineering-NTU Corporate Lab through the NRF corporate lab@university scheme. Publisher Copyright: {\textcopyright} 2018 IEEE.; 23rd IEEE International Conference on Digital Signal Processing, DSP 2018 ; Conference date: 19-11-2018 Through 21-11-2018",
year = "2019",
month = jan,
day = "31",
doi = "10.1109/ICDSP.2018.8631542",
language = "English",
series = "International Conference on Digital Signal Processing, DSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018",
}