Motorcyclists safety system to avoid rear end collisions based on acoustic signatures
| Autor: | Fabrice Meriaudeau, A. Saeed Malik, M. Zuki Yusoff, M. Naufal M. Saad, Muhammad Muzammel |
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| Rok vydání: | 2017 |
| Předmět: |
050210 logistics & transportation
Engineering Audio signal business.industry Feature vector 05 social sciences Real-time computing Short-time Fourier transform Reduction (complexity) 03 medical and health sciences 0302 clinical medicine Feature (computer vision) 0502 economics and business 11. Sustainability Principal component analysis Spectrogram Collision detection 030212 general & internal medicine business Simulation |
| Zdroj: | QCAV |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2266860 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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