relation: https://khub.utp.edu.my/scholars/9273/
title: Motorcyclists safety system to avoid rear end collisions based on acoustic signatures
creator: Muzammel, M.
creator: Yusoff, M.Z.
creator: Malik, A.S.
creator: Saad, M.N.M.
creator: Meriaudeau, F.
description: In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87. We believe that this method can help to reduce a significant number of motorcycle accidents.
publisher: SPIE
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Muzammel, M. and Yusoff, M.Z. and Malik, A.S. and Saad, M.N.M. and Meriaudeau, F.  (2017) Motorcyclists safety system to avoid rear end collisions based on acoustic signatures.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020314631&doi=10.1117%2f12.2266860&partnerID=40&md5=1aa219b34be68148f296f69912bb9069
relation: 10.1117/12.2266860
identifier: 10.1117/12.2266860