Title: Vision-Based Road Bump Detection Using a Front-Mounted Car Camcorder
Authors: Chen, Hua-Tsung
Lai, Chun-Yu
Hsu, Chun-Chieh
Lee, Suh-Yin
Lin, Bao-Shuh Paul
Ho, Chien-Peng
Department of Computer Science
Keywords: intelligent vehicle;driver assistance system;pattern recognition;signal processing;motion analysis
Issue Date: 1-Jan-2014
Abstract: Advanced vehicle safety is a recently emerging issue, appealed from the rapidly explosive population of car owners. Increasing driver assistance systems have been designed for warning drivers of what should be noticed by analyzing surrounding environments with sensors and/or cameras. As one of the hazard road conditions, road bumps not only damage vehicles but also cause serious danger, especially at night or under poor lighting conditions. In this paper we propose a vision-based road bump detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, the input video is transformed into a time-sliced image, which is a condensed video representation. Consequently, we estimate the vertical motion of the vehicle based on the time-sliced image and infer the existence of road bumps. Once a bump is detected, the location fix obtained from GPS is reported to a central server, so that the other vehicles can receive warnings when approaching the detected bumpy regions. Encouraging experimental results show that the proposed system can detect road bumps efficiently and effectively. It can be expected that traffic security will be significantly promoted through the mutually beneficial mechanism that a driver who is willing to report the bumps he/she meets can receive warnings issued from others as well.
URI: http://dx.doi.org/10.1109/ICPR.2014.776
ISBN: 978-1-4799-5208-3
ISSN: 1051-4651
DOI: 10.1109/ICPR.2014.776
Begin Page: 4537
End Page: 4542
Appears in Collections:Conferences Paper