Rear-end vision-based collision detection system for motorcyclists

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 introd...

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Main Authors: Muzammel, M., Yusoff, M.Z., Meriaudeau, F.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19513 /
Published: SPIE 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021698842&doi=10.1117%2f1.JEI.26.3.033002&partnerID=40&md5=d515d778107d2090c5d796b2b6120fec
http://eprints.utp.edu.my/19513/
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spelling utp-eprints.195132018-04-20T06:05:30Z Rear-end vision-based collision detection system for motorcyclists Muzammel, M. Yusoff, M.Z. Meriaudeau, F. 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. © 2017 SPIE and IS&T. SPIE 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021698842&doi=10.1117%2f1.JEI.26.3.033002&partnerID=40&md5=d515d778107d2090c5d796b2b6120fec Muzammel, M. and Yusoff, M.Z. and Meriaudeau, F. (2017) Rear-end vision-based collision detection system for motorcyclists. Journal of Electronic Imaging, 26 (3). http://eprints.utp.edu.my/19513/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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. © 2017 SPIE and IS&T.
format Article
author Muzammel, M.
Yusoff, M.Z.
Meriaudeau, F.
spellingShingle Muzammel, M.
Yusoff, M.Z.
Meriaudeau, F.
Rear-end vision-based collision detection system for motorcyclists
author_sort Muzammel, M.
title Rear-end vision-based collision detection system for motorcyclists
title_short Rear-end vision-based collision detection system for motorcyclists
title_full Rear-end vision-based collision detection system for motorcyclists
title_fullStr Rear-end vision-based collision detection system for motorcyclists
title_full_unstemmed Rear-end vision-based collision detection system for motorcyclists
title_sort rear-end vision-based collision detection system for motorcyclists
publisher SPIE
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021698842&doi=10.1117%2f1.JEI.26.3.033002&partnerID=40&md5=d515d778107d2090c5d796b2b6120fec
http://eprints.utp.edu.my/19513/
_version_ 1741196218693844992
score 11.62408

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