TY  - CONF
VL  - 10338
A1  - Muzammel, M.
A1  - Yusoff, M.Z.
A1  - Malik, A.S.
A1  - Saad, M.N.M.
A1  - Meriaudeau, F.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020314631&doi=10.1117%2f12.2266860&partnerID=40&md5=1aa219b34be68148f296f69912bb9069
PB  - SPIE
SN  - 0277786X
Y1  - 2017///
ID  - scholars9273
TI  - Motorcyclists safety system to avoid rear end collisions based on acoustic signatures
KW  - Accidents; Cost effectiveness; Motorcycles; Principal component analysis; Vision
KW  -  Acoustic information; Acoustic signals; Acoustic signature; Collision detection; Motorcycle accident; Rear-end collisions; Short time Fourier transforms; Spectrograms
KW  -  Quality control
N2  - 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.
N1  - cited By 3; Conference of 13th International Conference on Quality Control by Artificial Vision, QCAV 2017 ; Conference Date: 14 May 2017 Through 16 May 2017; Conference Code:127966
AV  - none
ER  -