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Journal of Electronic Imaging

Rear-end vision-based collision detection system for motorcyclists
Author(s): Muhammad Muzammel; Mohd Zuki Yusoff; Fabrice Meriaudeau
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Paper Abstract

In many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (2) online datasets (recorded by placing a camera at the front of a car). This method achieved 95.1% accuracy for the self-recorded dataset and gives reliable results for the rear-end vehicle detections under different road scenarios. This technique also performs better for the online car datasets. The proposed technique’s high detection accuracy using a monocular vision camera coupled with its low computational complexity makes it a suitable candidate for a motorbike rear-end collision detection system.

Paper Details

Date Published: 4 May 2017
PDF: 14 pages
J. Electron. Imag. 26(3) 033002 doi: 10.1117/1.JEI.26.3.033002
Published in: Journal of Electronic Imaging Volume 26, Issue 3
Show Author Affiliations
Muhammad Muzammel, Univ. Teknologi Petronas (Malaysia)
Univ. de Bourgogne Franche Comté (France)
Mohd Zuki Yusoff, Univ. Teknologi Petronas (Malaysia)
Fabrice Meriaudeau, Univ. Teknologi Petronas (Malaysia)
Univ. de Bourgogne Franche Comté (France)

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