Motorcyclists Safety System to avoid Rear End Collisions based on Acoustic Signatures

M Muzammel, 1, 2 M. Zuki Yusoff, 2 Aamir Saeed Malik 2 Mohamad Naufal Mohamad Saad 2 Fabrice Meriaudeau 1, 2, *
* Auteur correspondant
1 Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508]
2 CISIR - Centre for Intelligent Signal and Imaging Research (Universiti Teknologi Petronas)
Abstract : 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.
Keywords : motorcycle accidents motorcyclists safety acoustic signal collision detection systems spectrogram Malaysia Riders
Type de document :
Communication dans un congrès
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01577015
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 24 août 2017 - 16:01:39
Dernière modification le : vendredi 7 décembre 2018 - 16:48:04

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  • HAL Id : hal-01577015, version 1

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UNIV-BOURGOGNE | UNIV-BM | LE2I | UNIV-BM-THESE | UNIV-HESAM

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M Muzammel,, M. Zuki Yusoff,, Aamir Saeed Malik, Mohamad Naufal Mohamad Saad, Fabrice Meriaudeau. Motorcyclists Safety System to avoid Rear End Collisions based on Acoustic Signatures . 13th International Conference on Quality Control by Artificial Vision, May 2017, Tokyo, Japan. pp. UNSP 1033818. ⟨hal-01577015⟩

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