Rainy weather recognition from in-vehicle camera images for driver assistance
Open accessAbstract
We propose a weather recognition method from in-vehicle camera images that uses a subspace method to judge rainy weather by detecting raindrops on the windshield. "Eigendrops" represent the principal components extracted from raindrop images in the learning stage. Then the method detects raindrops by template matching. In experiments using actual video sequences, our method showed good detection ability of raindrops and promising results for rainfall judgment from detection results.
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Wu et al.'s proposed saliency-map algorithm performed better than Kurihata et al. [18] Eigendrops and Roser and Geiger [21] initial raindrop detection stage, both in terms of Precision and Recall metrics.
“Results showed that the proposed algorithm performed better than the other two, both in terms of Precision and Recall metrics.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [18]
Kurihata et al.'s Eigendrops model accounts for all variations of raindrop shape and size, and the effect of image background on raindrop appearance.
“This model does account for all variations of raindrop shape and size, and the effect of image background on raindrop appearance.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [18]
To create Eigendrops, the camera needs to be focused on the windshield, which means it cannot be used for other vision-based automotive applications.
“Furthermore, to achieve strong and distinctive characteristics that are necessary to create the Eigendrops, the camera needs to be focused on the windshield which means it cannot be used for other vision-based automotive applications.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [18]
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Improving driver pose estimation
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Attentive Generative Adversarial Network for Raindrop Removal from A Single Image
Qian, R., Tan, R. T., Yang, W., Su, J., & Liu, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00263 · Cited by 821 · see citing works
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Classification of weather situations on single color images
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Raindrop detection and removal using salient visual features
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A Review of Detection and Removal of Raindrops in Automotive Vision Systems
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“[18] Eigendrops and Roser and Geiger [21] initial raindrop detection stage, done with SURF.”
BackgroundMethodInfluentialDetection of Raindrop with Various Shapes on aWindshield
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- Authors
- Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., & Miyahara, T.
- Affiliations
- Nagoya University (JP)
- Denso (Japan) (JP)
- ORCID
- Venue
- IEEE Proceedings. Intelligent Vehicles Symposium, 2005.
- Publisher
- IEEE
- Published
- 2005
- Pages
- 205-210
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2113968972
- Cited by
- 177
- References
- 5
- Citations by year
- 2025: 11 · 2024: 8 · 2023: 9 · 2022: 13 · 2021: 19 · 2020: 22 · 2019: 19 · 2018: 14 · 2017: 10 · 2016: 6 · 2015: 7 · 2014: 4 · 2013: 4 · 2012: 5
- FWCI
- 6.63
- Type
- conference paper
- Language
- en
- Open access
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- Institutional Repositories DataBase (IRDB) (publishedVersion)
- Nagoya University Repository (NAGOYA Repository) (submittedVersion, cc-by-nc-nd)
- Funders
- Japan Society for the Promotion of Science
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- driver assistance, raindrop detection, windshields, subspace methods, principal component analysis, template matching, rain detection
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