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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun Z, Ban XJ (2013) Vehicle classification using GPS data. Transp Res Part C: Emerg Technol 37:102–117
Munich ME (2004) Bayesian subspace methods for acoustic signature recognition of vehicles. In: 2004 12th European signal processing conference, pp 2107-2110
George J, Cyril A, Koshy BI, Mary L (2013) Exploring sound signature for vehicle detection and classification using ANN. Int J Soft Comput 4:29
Gajda J, Sroka R, Stencel M, Wajda A, Zeglen T (2001) A vehicle classification based on inductive loop detectors. In: Proceedings of the 18th IEEE instrumentation and measurement technology conference, IMTC 2001, vol 1, pp 460–464
Kaewkamnerd S, Pongthornseri R, Chinrungrueng J, Silawan T (2009) Automatic vehicle classification using wireless magnetic sensor. IEEE international workshop on intelligent data acquisition and advanced computing systems: technology and applications, IDAACS 2009:420–424
Lai JC, Huang SS, Tseng CC (2010) Image-based vehicle tracking and classification on the highway. In: 2010 international conference on green circuits and systems (ICGCS), pp 666–670
Zhang W, Wu QJ, Yang X, Fang X (2008) Multilevel framework to detect and handle vehicle occlusion. IEEE Trans Intell Transp Syst 9:161–174
Urazghildiiev I et al (2002) A vehicle classification system based on microwave radar measurement of height profiles
Bhoraskar R, Vankadhara N, Raman B, Kulkarni P (2012) Wolverine: traffic and road condition estimation using smartphone sensors. In: 2012 fourth international conference on communication systems and networks (COMSNETS), pp 1–6
Singh P, Juneja N, Kapoor S (2013) Using mobile phone sensors to detect driving behavior. In: Proceedings of the 3rd ACM symposium on computing for development, p 53
Tyagi V, Kalyanaraman S, Krishnapuram R (2012) Vehicular traffic density state estimation based on cumulative road acoustics. IEEE Trans Intell Transp Syst 13:1156–1166
Ali HM, Alwan ZS (2017) Car accident detection and notification system using smartphone. LAP LAMBERT Academic Publishing, Saarbrucken
Paulraj M, Adom AH, Sundararaj S, Rahim NBA (2013) Moving vehicle recognition and classification based on time domain approach. Procedia Eng 53:405–410
Sen R, Raman B, Sharma P (2010) Horn-ok-please. In: Proceedings of the 8th international conference on mobile systems, applications, and services, pp 137–150
Sen R, Siriah P, Raman B (2011) Roadsoundsense: acoustic sensing based road congestion monitoring in developing regions. In: 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON), pp 125–133
Kaur A, Sood N, Aggarwal N, Vij D, Sachdeva B (2017) Traffic state detection using smartphone based acoustic sensing. J Intell Fuzzy Syst 32:3159–3166
Vij D, Aggarwal N (2018) Smartphone based traffic state detection using acoustic analysis and crowdsourcing. Appl Acoust 138:80–91
George J, Mary L, Riyas K (2013) Vehicle detection and classification from acoustic signal using ANN and KNN. In: 2013 international conference on control communication and computing (ICCC), pp 436–439
Kandpal M, Kakar VK, Verma G (2013) Classification of ground vehicles using acoustic signal processing and neural network classifier. In: 2013 international conference on signal processing and communication (ICSC), pp 512–518
Maciejewski H, Mazurkiewicz J, Skowron K, Walkowiak T (1997) Neural networks for vehicle recognition. In: Proceeding of the 6th international conference on microelectronics for neural networks, evolutionary and fuzzy systems, p 5
Aggarwal N, Vij D, Soni S (2017) Acoustic vehicular data. Retrieved from http://pudataset.puchd.ac.in:8080/jspui/handle/123456789/14. Accessed on 10 Apr 2018
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bansal, A., Aggarwal, N., Vij, D., Sharma, A. (2019). An Off the Shelf CNN Features Based Approach for Vehicle Classification Using Acoustics. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_110
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_110
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)