Classification of weather situations on single color images
Open accessAbstract
Present vision based driver assistance systems are designed to perform under good-natured weather conditions. However, limited visibility caused by heavy rain or fog strongly affects vision systems. To improve machine vision in bad weather situations, a reliable detection system is necessary as a ground base. We present an approach that is able to distinguish between multiple weather situations based on the classification of single monocular color images, without any additional assumptions or prior knowledge. The proposed image descriptor clearly outperforms existing descriptors for that task. Experimental results on real traffic images are characterized by high accuracy, efficiency, and versatility with respect to driver assistance systems.
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Roser and Moosmann's classifier showed a good rate of classification for the highway-only subset (2% error) but the classification rate got worse as the complexity of the driving environment increased.
“Results showed a good rate of classification for the highway-only subset (2% error) but the classification rate got worse as the complexity of the driving environment increased.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [16]
Roser and Moosmann's classifier had some misclassification errors for adjacent classes (e.g., "clear" and "light rain" or "Heavy rain" and "light rain").
“In addition, for adjacent classes (e.g., “clear” and “light rain” or “Heavy rain” and “light rain”), some misclassification errors were reported.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [16]
Roser and Moosmann's system does not detect individual raindrops on vehicle windshield, which makes it less appropriate for ADAS and autonomous driving applications.
“Roser and Moosmann’s system does not detect individual raindrops on vehicle windshield which makes it less appropriate for ADAS and autonomous driving applications.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [16]
Some background objects were mistakenly detected as raindrops, which skewed the results of Roser and Moosmann's classifier.
“Some background objects were also mistakenly detected as raindrops, which skewed the results of the classifier.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [16]
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References 21 · Cited by 132
- Authors
- Roser, M., & Moosmann, F.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- 2008 IEEE Intelligent Vehicles Symposium
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 798-803
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2154673940
- Cited by
- 132
- References
- 21
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 16 · 2022: 14 · 2021: 16 · 2020: 9 · 2019: 16 · 2018: 9 · 2017: 7 · 2016: 9 · 2015: 7 · 2014: 4 · 2013: 2 · 2012: 4
- FWCI
- 1.00
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- other-oa
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (publishedVersion)
- KITopen (other-oa)
- Funders
- Deutsche Forschungsgemeinschaft
- 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 › Image Retrieval and Classification Techniques
- Keywords
- driver assistance systems, heavy rainfall, fog, image descriptors, traffic images
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