Vision based speed breaker detection for autonomous vehicle

Arvind C.S.; Ritesh Mishra; Kumar Vishal; Venugopal Gundimeda

doi:10.1117/12.2311315

13 April 2018 Vision based speed breaker detection for autonomous vehicle

Arvind C.S., Ritesh Mishra, Kumar Vishal, Venugopal Gundimeda

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.

Citation Download Citation

Arvind C.S., Ritesh Mishra, Kumar Vishal, and 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); https://doi.org/10.1117/12.2311315

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available