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All Round Blind Spot Detection by Lens Condition Adaptation based on Rearview Camera Images
Technical Paper
2013-01-0622
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper deals with a vehicle detection method for realizing a blind spot warning function, under various environmental conditions. We introduced a method that is capable of discriminating the target object vehicles, under poor lighting conditions and in cases where the lens may be exposed to splashes in wet, snow and dirt roads. The image sensing of the vehicle detection consists of four functional components: obstacle detection, velocity estimation, vertical edge detection, and final classification. Such componets allow robust performances resembling geometry based approaches, with low calculation power as an apperance based approach. This paper describes the functional components, and furthermore methods to enhance the performances under low contrast conditions and also suppress false detections caused by residue on the lens, which becomes essential for installation on vehicles driven in actual road conditions.
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Citation
Hayakawa, Y. and Fukata, O., "All Round Blind Spot Detection by Lens Condition Adaptation based on Rearview Camera Images," SAE Technical Paper 2013-01-0622, 2013, https://doi.org/10.4271/2013-01-0622.Also In
References
- MATSUMOTO S. etal. Development of The Nissan ASV-2, ESV 2001 Vehicle Body Slip Angle Estimation Using an Adaptive Observer.M.Kaminaga etal. AVEC (Advanced Vehicle Control) 1998 Nagoya, Japan 231 234
- Hayakawa Y. , Sato , K. , and Kobayashi , M. A Blind Spot Assistance System Based on Direct-Yaw-Moment Control SAE Technical Paper 2011-01-0202 2011 104271/2011-01-0202
- Kuehnel , A. Symmetry-based recognition for vehicle rears Pattern Recognition Letters 12 249 258 1991
- Betke , M. , Haritaoglu , E. , and Davis , L. Real-time multiple vehicle detection and tracking from a moving vehicle Machine Vision and Applications 12 2 69 83 2000
- Haselhoff , A. , Kummert , A. , and Schneider , G. Haar-like Feature and AdaBoost Approach European Signal Processing Conference (EUSIPCO 2007) 2070 2074 2007
- Gepperth , A. , Edelbrunner , J. , and Bücher , T. Real-time detection and classification of cars in video sequences Intelligent Vehicles Symposium 625 631 2005
- Enkelmann , W. Obstacle Detection by Evaluation of Optical Flow Fields from Image Sequences Image and Vision Computing 9 3 160 168 1991
- Batavia , P. H. , Pomerleau , D. A. , and Thorpe , C. E. Overtaking Vehicle Detection Using Implicit Optical Flow Proc. of the IEEE Intelligent Transportation Systems Conference 729 734 1997
- Takemura , M ane Departure Warning in Various Severe Lens Conditions Vision Engineering Workshop 2012 IS1 B5