eprintid: 8673
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/86/73
datestamp: 2023-11-09 16:20:35
lastmod: 2023-11-09 16:20:35
status_changed: 2023-11-09 16:13:15
type: article
metadata_visibility: show
creators_name: Muzammel, M.
creators_name: Yusoff, M.Z.
creators_name: Meriaudeau, F.
title: Rear-end vision-based collision detection system for motorcyclists
ispublished: pub
keywords: Cameras; Hough transforms; Motorcycles; Roads and streets; Transportation, Collision detection; Detection accuracy; Harris corner detection; Low computational complexity; Motorcycle accident; Rear-end collisions; Road detection; Sobel edge detection, Edge detection
note: cited By 9
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. © 2017 SPIE and IS&T.
date: 2017
publisher: SPIE
official_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
id_number: 10.1117/1.JEI.26.3.033002
full_text_status: none
publication: Journal of Electronic Imaging
volume: 26
number: 3
refereed: TRUE
issn: 10179909
citation:   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).   ISSN 10179909