Abstract
This chapter outlines a robust algorithm to detect the arrival of a vehicle of arbitrary type when other noises are present. It is done via analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. To achieve it with minimum number of false alarms, a construction of a training database of acoustic signatures of signals emitted by vehicles using the distribution of the energies among blocks of wavelet packet coefficients (waveband spectra, see Sect. 4.6) is combined with a procedure of random search for a near-optimal footprint (RSNOFP). The number of false alarms in the detection is minimized even under severe conditions such as: signals emitted by vehicles of different types differ from each other, whereas the set of non-vehicle recordings (the training database) contains signals emitted by planes, helicopters, wind, speech, steps etc. The described algorithm is robust even when the tested conditions are completely different from the conditions where the training signals were recorded. This technique has many algorithmic variations. For example, it can be used to distinguish among different types of vehicles. The described algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real-time detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Averbuch, E. Hulata, V. Zheludev, I. Kozlov, A wavelet packet algorithm for classification and detection of moving vehicles. Multidimens. Syst. Signal Process. 12(1), 9–31 (2001)
A. Averbuch, I. Kozlov, V. Zheludev, Wavelet-packet-based algorithm for identification of quasi-periodic signals, in Wavelets: Applications in Signal and Image Processing IX, Proceedings of the SPIE, vol. 4478, ed. by A.F. Laine, M.A. Unser, A. Aldroubi (2001), pp. 353–360
A. Averbuch, V. Zheludev, N. Rabin, A. Schclar, Wavelet-based acoustic detection of moving vehicles. Multidimens. Syst. Signal Process. 20(1), 55–80 (2009)
A. Averbuch, V. Zheludev, P. Neittaanmäki, P. Wartiainen, K. Huoman, K. Janson, Acoustic detection and classification of river boats. Appl. Acoust. 72(1), 22–34 (2011)
A. Averbuch, N. Rabin, A. Schclar, V. Zheludev, Dimensionality reduction for detection of moving vehicles. Pattern Anal. Appl. 15, 19–27 (2012)
A.Z. Averbuch, P. Neittaanmäki, V.A. Zheludev, Spline and Spline Wavelet Methods with Applications to Signal and Image Processing, Volume I: Periodic Splines (Springer, Berlin, 2014)
L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees (Chapman & Hall, New York, 1993)
D. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52 (2006)
D. Donoho, Y. Tsaig, Extensions of compressed sensing. Signal Process. 86(3), 533–548 (2006)
J. Romberg, E. Candes, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf. Theory 52(2), 489–509 (2006)
A. Schclar, A. Averbuch, N. Rabin, V. Zheludev, K. Hochman, A diffusion framework for detection of moving vehicles. Digit. Signal Process. 20(1), 111–122 (2010)
M.V. Wickerhauser, Adapted Wavelet Analysis: from Theory to Software (AK Peters, Wellesley, 1994)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Averbuch, A.Z., Neittaanmäki, P., Zheludev, V.A. (2019). Acoustic Detection of Moving Vehicles. In: Spline and Spline Wavelet Methods with Applications to Signal and Image Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-92123-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-92123-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92122-8
Online ISBN: 978-3-319-92123-5
eBook Packages: EngineeringEngineering (R0)