13 April 2018 Vision based speed breaker detection for autonomous vehicle
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960E (2018) https://doi.org/10.1117/12.2311315
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of ~0.98.
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Arvind C.S., Arvind C.S., Ritesh Mishra, Ritesh Mishra, Kumar Vishal, Kumar Vishal, Venugopal Gundimeda, Venugopal Gundimeda, } "Vision based speed breaker detection for autonomous vehicle", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960E (13 April 2018); doi: 10.1117/12.2311315; https://doi.org/10.1117/12.2311315
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