A novel technique for raindrop detection on a car windshield using geometric-photometric model
Abstract
In this paper, we describe a novel technique for detecting raindrops using in-vehicle camera images. The appearance of raindrops on a car windshield can depend on their background, so it is often difficult to detect them using conventional template matching methods, which are based on image features. Initially, we extract potential raindrop regions from images, before generating a rendered background image using a physical raindrop model based on the refraction of light rays. This rendered image is then used to identify true raindrops based on their similarity to the true background image. We propose a new model that approximates a raindrop shape as a spheroid section. This method can represent different raindrop shapes more adaptively and flexibly than conventional models, which approximate raindrops as a section of a sphere. We also extend the Maximally Stable External Regions algorithm to extract candidate raindrops and we identify three measures of image similarity using a Support Vector Machine algorithm. We conducted experiments that confirmed the effectiveness of the proposed technique.
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The main drawback of the eMSER approach is that it does not describe any mechanism for rainy image restoration.
“The main drawback of this approach is that it does not describe any mechanism for rainy image restoration.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [43]
Sugimoto et al. used the eMSER algorithm to detect potential raindrop blobs.
“Sugimoto et al. [43] used Extended Maximally Stable External Region (eMSER) algorithm to detect potential raindrop blobs.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [43]
Of the three evaluated, SURF performed worse and eMSER showed the best results, with precision and recall rates ranging from (0.8, 0.38) to (0.68, 0.5).
“Of the three evaluated, SURF performed worse and eMSER showed the best results, with rates for (precision, recall) ranging from (0.8, 0.38) to (0.68, 0.5), respectively.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [43]
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,305 · see citing works
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547 · see citing works
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,683 · see citing works
Garg, K., & Nayar, S. K. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0028-6 · Cited by 477 · see citing works
Video-based raindrop detection for improved image registration
Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops · doi:10.1109/iccvw.2009.5457650 · Cited by 126 · see citing works
Halimeh, J. C., & Roser, M. (2009) · 2009 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164347 · Cited by 76 · see citing works
Realistic Modeling of Water Droplets for Monocular Adherent Raindrop Recognition Using Bézier Curves
Roser, M., Kurz, J., & Geiger, A. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-22819-3_24 · Cited by 63 · see citing works
Detection of unfocused raindrops on a windscreen using low level image processing
Nashashibi, F., de Charrette, R., & Lia, A. (2010) · 2010 11th International Conference on Control Automation Robotics & Vision · doi:10.1109/icarcv.2010.5707398 · Cited by 38 · see citing works
Intelligent traffic sign detector: Adaptive learning based on online gathering of training samples
Deguchi, D., Shirasuna, M., Doman, K., et al. (2011) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2011.5940408 · Cited by 27 · see citing works
Vehicle Detection Based on Perspective Transformation Using Rear-View Camera
Tanaka, S., Yamada, K., Ito, T., et al. (2011) · International Journal of Vehicular Technology · doi:10.1155/2011/279739 · Cited by 22 · see citing works
On-board Pedestrian Detection by the Motion and the Cascaded Classifiers
Shibayama, Y., Kim, H., Fujimura, K., et al. (2011) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-011-0034-0 · Cited by 4 · see citing works
Raindrop removal from in-vehicle camera images based on matching adjacent frames
Inaba, H., Masakuni, O., & Kamata, S. (2011) · IEICE Technical Report; IEICE Tech. Rep. · Cited by 1 · see citing works
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A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 21 · see citing works
“[43] used Extended Maximally Stable External Region (eMSER) algorithm to detect potential raindrop blobs.”
MethodDetection of Raindrop with Various Shapes on aWindshield
Ishizuka, J., & Onoguchi, K. (2016) · Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0005796004750483 · Cited by 5 · see citing works
“(Sugimoto et al., 2012) extended this assumption to a spheroid section.”
BackgroundMethod
- Authors
- Sugimoto, M., Kakiuchi, N., Ozaki, N., & Sugawara, R.
- Affiliations
- The University of Tokyo (JP)
- Denso (Japan) (JP)
- Venue
- 2012 15th International IEEE Conference on Intelligent Transportation Systems
- Publisher
- IEEE
- Published
- 2012-09
- Volume
- 10
- Pages
- 740-745
- OpenAlex
- W1989348191
- Cited by
- 6
- References
- 14
- Citations by year
- 2024: 1 · 2021: 1 · 2020: 1 · 2017: 1 · 2016: 1 · 2013: 1
- FWCI
- 0.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- raindrop detection, light refraction, candidate region extraction, image similarity measures, support vector machine, in-vehicle camera, template matching
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