Rear-end vision-based collision detection system for motorcyclists

Muhammad Muzammel; Mohd Zuki Yusoff; Fabrice Meriaudeau

doi:10.1117/1.JEI.26.3.033002

4 May 2017 Rear-end vision-based collision detection system for motorcyclists

Muhammad Muzammel, Mohd Zuki Yusoff, Fabrice Meriaudeau

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 3, 033002 (May 2017). https://doi.org/10.1117/1.JEI.26.3.033002

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.

Citation Download Citation

Muhammad Muzammel, Mohd Zuki Yusoff, and Fabrice Meriaudeau "Rear-end vision-based collision detection system for motorcyclists," Journal of Electronic Imaging 26(3), 033002 (4 May 2017). https://doi.org/10.1117/1.JEI.26.3.033002

Received: 4 January 2017; Accepted: 13 April 2017; Published: 4 May 2017

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