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An Off the Shelf CNN Features Based Approach for Vehicle Classification Using Acoustics

  • Anam BansalEmail author
  • Naveen Aggarwal
  • Dinesh Vij
  • Akashdeep Sharma
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Vehicle classification is a trending area of research in Intelligent Transport System. Vehicle Recognition can help traffic policy makers, public safety organizations, insurance companies, etc. It can assist in various applications like automatic toll collection, emissions/pollution estimation, traffic modelling, etc. Many methods, both infrastructure-based and infrastructureless have been proposed for vehicle classification but they have certain disadvantages. In this paper, we have explored the possibility to use off the shelf Convolutional Neural Network (CNN) features for commuter vehicle classification using acoustics. To extract features from acoustic recordings taken from the vehicle, a simple CNN is designed. These features are used to classify vehicles in five main categories car, bus, plane, train, and three-wheeler using Support Vector Machine (SVM). This approach is tested on dataset having 4789 recordings and gives good accuracy as compared to simple Mel Frequency Cepstral Coefficients (MFCC) feature based deep learning and machine learning approach.

Keywords

Vehicle Classification Acoustic signals Convolutional Neural Network Support Vector Machine Off the shelf CNN features based approach 

Notes

Acknowledgements

This work is undertaken as a part of the project ‘CARTS—Communication Assisted Road Transportation System’ funded by ITRA, Media Lab Asia; and Design Innovation Center, Panjab University, Chandigarh, India.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anam Bansal
    • 1
    Email author
  • Naveen Aggarwal
    • 1
  • Dinesh Vij
    • 1
  • Akashdeep Sharma
    • 1
  1. 1.UIET, Panjab UniversityChandigarhIndia

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