Detection of unfocused raindrops on a windscreen using low level image processing
Open accessAbstract
In a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The algorithm correctly detected rain situations as heavy, medium, or no-rain.
“Test results showed that the algorithm correctly detected rain situations as heavy, medium, or no-rain.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [40]
Nashashibi et al. used blurriness to detect adherent raindrops on windshields.
“Nashashibi et al. [40] used this characteristic to detect adherent raindrops on windshields.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [40]
Nashashibi et al. did not present data showing how accurate the system was in detecting individual raindrops.
“Though promising, Nashashibi et al. did not present data to show how accurate the system was in detecting individual raindrops.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [40]
Cites
Narasimhan, S. G., & Nayar, S. K. (2002) · International Journal of Computer Vision · doi:10.1023/a:1016328200723 · Cited by 1,734 · see citing works
Detection and removal of rain from videos
Garg, K., & Nayar, S. K. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315077 · Cited by 531 · 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
THE PRODUCTION OF RAIN BY A CHAIN REACTION IN CUMULUS CLOUDS AT TEMPERATURES ABOVE FREEZING
Langmuir, I. (1948) · Journal of Meteorology · doi:10.1175/1520-0469(1948)005 · Cited by 351 · see citing works
Rain Removal in Video by Combining Temporal and Chromatic Properties
Zhang, X., Li, H., Qi, Y., Leow, W. K., & Ng, T. (2006) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2006.262572 · Cited by 298 · see citing works
Rainy weather recognition from in-vehicle camera images for driver assistance
Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., & Miyahara, T. (2005) · IEEE Proceedings. Intelligent Vehicles Symposium, 2005. · doi:10.1109/ivs.2005.1505103 · Cited by 177 · see citing works
Cozman, F. G., & Krotkov, E. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1997.609419 · Cited by 161 · see citing works
Narasimhan, S. G., & Nayar, S. K. (2008) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1508044.1508113 · Cited by 101 · see citing works
Blind blur assessment for vision-based applications
Wu, S., Lin, W., Xie, S., Lu, Z., Ong, E. P., & Yao, S. (2009) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2009.03.002 · Cited by 58 · see citing works
Vision-based rain sensing with an in-vehicle camera
Gormer, S., Kummert, A., Park, S.-B., & Egbert, P. (2009) · 2009 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164291 · Cited by 43 · see citing works
The Application of Histogram on Rain Detection in Video
Zhao, X., Liu, P., Liu, J., et al. (2008) · Joint Conference on Information Sciences (JCIS) · doi:10.2991/jcis.2008.65 · Cited by 22 · see citing works
Development of Vision based Control Smart Windwhield Wiper System for Intelligent Vehicle
Park, J.-H., Kim, M.-H., Im, H.-J., et al. (2006) · SICE-ICASE International Joint Conference · doi:10.1109/sice.2006.314662 · Cited by 11 · see citing works
Nayar, S. K., & Narasimhan, S. G. (2005) · Springer tracts in advanced robotics · doi:10.1007/11008941_36 · Cited by 3 · see citing works
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Detecting unfocused raindrops: In-vehicle multipurpose cameras
Cord, A., & Gimonet, N. (2014) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2013.2287451 · Cited by 35 · see citing works
Raindrops on Windshield: Dataset and Lightweight Gradient-Based Detection Algorithm
Soboleva, V., & Shipitko, O. (2021) · 2021 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659915 · Cited by 30 · see citing works
Towards rain detection through use of in-vehicle multipurpose cameras
Cord, A., & Aubert, D. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940484 · Cited by 25 · see citing works
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
“[40] used this characteristic to detect adherent raindrops on windshields.”
BackgroundMethodA novel technique for raindrop detection on a car windshield using geometric-photometric model
Sugimoto, M., Kakiuchi, N., Ozaki, N., & Sugawara, R. (2012) · 2012 15th International IEEE Conference on Intelligent Transportation Systems · doi:10.1109/itsc.2012.6338613 · Cited by 6 · see citing works
Detection 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
“(Nashashibi et al., 2010) detected unfocus raindrops using the similar characteristics.”
Method
- Authors
- Nashashibi, F., de Charrette, R., & Lia, A.
- Affiliations
- Centre de Robotique (FR)
- ORCID
- Nashashibi, Fawzi
- Venue
- 2010 11th International Conference on Control Automation Robotics & Vision
- Publisher
- IEEE
- Published
- 2010-12
- Pages
- 1410-1415
- OpenAlex
- W2150156615
- Cited by
- 38
- References
- 14
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 2 · 2021: 6 · 2020: 6 · 2019: 6 · 2018: 2 · 2017: 2 · 2016: 3 · 2014: 2 · 2013: 1 · 2012: 4
- FWCI
- 3.53
- Type
- conference paper
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- International Conference on Control, Automation, Robotics and Vision (ICARCV) (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- raindrop detection, windscreen, transparent screens, spatio-temporal analysis, real-time detection, low-level image processing, intelligent transportation systems, onboard camera
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