A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Authors

  • Sergei Astapov Laboratory for Proactive Technologies, Tallinn, University of Technology, Ehitajate tee 5, 19086, Tallinn
  • Andri Riid Laboratory for Proactive Technologies, Tallinn, University of Technology, Ehitajate tee 5, 19086, Tallinn

Abstract

Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.

References

P. E. William and M. W. Hoffman, “Classification of military ground vehicles using time domain harmonics’ amplitudes,” vol. 60, no. 11, pp. 3720-3731, 2011. [Web of Science]

E.-H. Ng, S.-L. Tan, and J. G. Guzman, “Road traffic monitoring using a wireless vehicle sensor network,” in Proc. Int. Symp. Intelligent SignalProcessing and Communications Systems ISPACS 2008, 2009, pp. 1-4.

A. Klausner, A. Tengg, and B. Rinner, “Vehicle classification on multisensor smart cameras using feature- and decision-fusion,” in Proc. FirstACM/IEEE Int. Conf. Distributed Smart Cameras ICDSC ’07, 2007, pp. 67-74.

T. Takechi, K. Sugimoto, T. Mandono, and H. Sawada, “Automobile identification based on the measurement of car sounds,” in Proc. 30thAnnual Conf. of IEEE Industrial Electronics Society IECON 2004, vol. 2, 2004, pp. 1784-1789.

A. Starzacher and B. Rinner, “Single sensor acoustic feature extraction for embedded realtime vehicle classification,” in Proc. Int Parallel andDistributed Computing, Applications and Technologies Conf, 2009, pp. 378-383.

N. A. Rahim, M. P. Paulraj, A. H. Adom, and S. Sundararaj, “Moving vehicle noise classification using backpropagation algorithm,” in Proc.6th Int Signal Processing and Its Applications (CSPA) Colloquium, 2010, pp. 1-6.

S. Maithani and R. Tyagi, “Noise characterization and classification for background estimation,” in Proc. Int. Conf. Signal Processing,Communications and Networking ICSCN ’08, 2008, pp. 208-213.

S. S. Yang, Y. G. Kim, and H. Choi, “Vehicle identification using wireless sensor networks,” in Proc. IEEE SoutheastCon, 2007, pp. 41-46.

G. Gritsch, N. Donath, B. Kohn, and M. Litzenberger, “Night-time vehicle classification with an embedded, vision system,” in Proc. 12th Int. IEEE Conf. Intelligent Transportation Systems ITSC ’09, 2009, pp. 1-6.

V. Cevher, R. Chellappa, and J. H. McClellan, “Vehicle speed estimation using acoustic wave patterns,” Trans. Sig. Proc., vol. 57, no. 1, pp. 30-47, Jan. 2009.

M. Zivanovic, A. R¨obel, and X. Rodet, “Adaptive threshold determination for spectral peak classification,” Comput. Music J., vol. 32, no. 2, pp. 57-67, Jun. 2008. [Web of Science]

M. Frigo and S. G. Johnson, “FFTw: an adaptive software architecture for the FFT,” in Proc. IEEE Int Acoustics, Speech and Signal ProcessingConf, vol. 3, 1998, pp. 1381-1384.

G. Peeters, “A large set of audio features for sound description (similarity and classification) in the cuidado project,” CUIDADO I.S.T. Project Report, Tech. Rep., 2004.

A. Riid and E. Rustern, “An integrated approach for the identification of compact, interpretable and accurate fuzzy rule-based classifiers from data,” in Proc. 15th IEEE Int Intelligent Engineering Systems (INES)Conf, 2011, pp. 101-107.

R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete mathematics: afoundation for computer science. Addison-Wesley Reading, MA, 1994, vol. 2.

S. Astapov, J. S. Preden, and E. Suurjaak, “A method of real-time mobile vehicle identification by means of acoustic noise analysis implemented on an embedded device,” in Proc. 13th Biennial Baltic Electronics Conf.(BEC), 2012, pp. 283-286.

Y. Peng and P. Flach, “Soft discretization to enhance the continuous decision tree induction,” in Integrating Aspects of Data Mining, DecisionSupport and Meta-Learning, C. Giraud-Carrier, N. Lavrac, and S. Moyle, Eds. ECML/PKDD’01 workshop notes, September 2001, pp. 109-118.

V. C. Ravindra, Y. Bar-Shalom, and T. Damarla, “Feature-aided localization of ground vehicles using passive acoustic sensor arrays,” in Proc.12th Int. Conf. Information Fusion FUSION ’09, 2009, pp. 70-77.

Downloads

Published

2015-03-11

Issue

Section

ARCHIVES / BACK ISSUES