Volume List  / Volume 3 (3)

Article

REVIEW ON VEHICULAR SPEED, DENSITY ESTIMATION AND CLASSIFICATION USING ACOUSTIC SIGNAL

DOI: 10.7708/ijtte.2013.3(3).08


3 / 3 / 331-343 Pages

Author(s)

Prashant Borkar - Department of Computer Science and Engineering, G.H. Raisoni College of Engineering, Nagpur, India -

Latesh G. Malik - Department of Computer Science and Engineering, G.H. Raisoni College of Engineering, Nagpur, India -


Abstract

Traffic monitoring and parameters estimation from urban to non urban (battlefield environment) traffic is fast-emerging field based on acoustic signals. We present here a comprehensive review of the state-of-the-art acoustic signal for vehicular speed estimation, density estimation and classification, critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). In recent years video onitoring and surveillance systems has been widely used in traffic management and hence traffic parameters can be achieved using such systems, but installation, operational and maintenance cost associated with these approaches are relatively high compared to the use of acoustic signal which is having very low installation and maintenance cost. The classification process includes sensing unit, class definition, feature extraction, classifier application and system evaluation. The acoustic classification system is part of a multi sensor real time environment for traffic surveillance and monitoring. Classification accuracy achieved by various studied algorithms shows very good performance for the ‘Heavy Weight’ class of vehicles as compared to the other category “Light Weight”. Also a slight performance degrades as vehicle speed increases. Vehicular speed estimation corresponds to average speed and traffic density measurement, and can be substantially used for traffic signal timings optimization.


Download Article

Number of downloads: 2705


Acknowledgements:

Authors are thankful to the Director of GHRCE, for proving infrastructure to carry out research work and also to the anonymous reviewers for many helpful suggestions.


References:

Amman, S.A.; Das, M. 2001. An Efficient Technique for Modeling and Synthesis of Automotive Engine Sounds, Industrial Electronics, IEEE Transactions. DOI: http://dx.doi.org/10.1109/41.904583, 48(1): 225-234.

 

Anami, B.S.; Pagi, V.B.; Magi, S.M. 2012. Comparative performance analysis of three classifiers for acoustic signal-based recognition of motorcycles using time- and frequency-domain features, Intelligent Transport Systems, IET. DOI: http://dx.doi.org/10.1049/iet-its.2011.0162, 6(3): 235-242.

 

Cevher, V.; Chellappa, R.; McCllelan, J.H. 2007. Joint Acoustic-Video Fingerprinting of Vehicles, PART I. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. DOI: http://dx.doi.org/10.1109/ICASSP.2007.366343, 5: 745-748.

 

Cevher, V.; Chellappa, R.; McCllelan, J.H. 2009. Vehicle Speed Estimation Using Acoustic Wave Patterns, IEEE Transactions on Signal Processing. DOI: http://dx.doi.org/10.1109/TSP.2008.2005750, 57(1): 30-47.

 

Chen, S.; Sun, Z.P. 1997. Traffic sensing by passive sound detection. In Proceedings of the VIII Conference on Sensors and their Applications, Glasgow, Scotland, U.K.

 

Chen, S.; Sun, Z.P.; Bridge, B. 1997. Automatic traffic monitoring by intelligent sound detection. In Proceedings of the IEEE Conference on Intelligent Transportation System. DOI: http://dx.doi.org/10.1109/ITSC.1997.660470, 171-176.

 

Chen, S.; Sun, Z.; Bridge, B. 2001. Traffic Monitoring Using Digital Sound Field Mapping, IEEE Transactions on Vehicular Technology. DOI: http://dx.doi.org/10.1109/25.966587, 50(6): 1582-1589.

 

Choe, H.; Karlsen, R.; Gerhart, G.; Meitzler, T. 1996. Wavelet based ground vehicle recognition using acoustic signals. In Proceedings of the SPIE, 2762: 434-445.

 

Coifman, B.; Beymer, D.; McLauchlan, P.; Malik, J. 1998. A real-time computer vision system for vehicle tracking and traffic surveillance, Transportation Research Part C: Emerging Technologies. DOI: http://dx.doi.org/10.1016/S0968-090X(98)00019-9, 6(4): 271-288.

 

Couvreur, C.; Bresler, Y. 1997. Doppler-based motion estimation for wide-band sources from single passive sensor measurements. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. DOI: http://dx.doi.org/10.1109/ICASSP.1997.604629, 5: 3537-3540.

 

Cucchiara, R.; Piccardi, M.; Mello, P. 2000. Image analysis and rule based reasoning for a traffic monitoring system, IEEE Transactions on Intelligent Transportation Systems. DOI: http://dx.doi.org/10.1109/6979.880969, 1(2): 119-130.

 

Damarla, T.R.; Mirelli, V. 2006. Distributed acoustic sensor data processing for target classification. In Proceedings of the SPIE-Unattended Ground, Sea and Air Sensor Technologies and Applications. DOI: http://dx.doi.org/10.1117/12.664585, 6231: 623105.

 

Duda, R.O.; Hart, P.E.; Stork, D.G. 2001. Pattern Classification, John Wiley & Sons Ltd.

 

Eskridge, R.E.; Hunt, J.C.R. 1979. Highway Modeling. Part I: Prediction of Velocity and Turbulence Fields in the Wake of Vehicles, American Meteorological Society. DOI: http://dx.doi.org/10.1175/1520-0450(1979)018<0387:HMPIPO>2.0.CO;2, 18(4): 387-400.

 

Graf, R.A.G.; Kuo, C.Y.; Dowling, A.P.; Graham, W.R. 2002. On the horn effect of a tyre/road interface-Part I: Experiment and computation, Journal of Sound and Vibration. DOI: http://dx.doi.org/10.1006/jsvi.2001.4238, 256(3): 417-431.

 

Kamijo, S.; Matsushita, Y.; Ikeuchi, K. 2000. Traffic monitoring and accident detection at intersections, IEEE Transaction on Intelligent Transportation Systems. DOI: http://dx.doi.org/10.1109/6979.880968, 1(2): 108-118.

 

Kastner-Klein, P.; Berkowicz, R.; Plate, E.J. 1998. Modelling of vehicle induced turbulence in air pollution studies for streets. In Proceedings of the 5th Workshop on Harmonisation Within Atmospheric Dispersion Model-ling for Regulatory Purposes, Rhodes, Greece.

 

Kato, J. 2005. An Attempt to Acquire Traffic Density by Using Road Traffic Sound. In Proceedings of the 2005 International Conference on Active Media Technology. DOI: http://dx.doi.org/10.1109/AMT.2005.1505370, 353-358.

 

Kuo, C.Y.; Graf, R.A.G.; Dowling, A.P.; Graham, W.R. 2002. On the horn effect of a tyre/road interface-Part II: Asymptotic theories, Journal of Sound and Vibration. DOI: http://dx.doi.org/10.1006/jsvi.2001.4217, 256(3): 433-445.

 

Kuo, S.M.; Morgan, D.R. 1999. Active noise control: A tutorial review. In Proceedings of the IEEE. DOI: http://dx.doi.org/10.1109/5.763310, 87(6): 943-973.

 

Lake, D. 1999. Efficient maximum likelihood estimation for multiple and coupled harmonics, Army Research Laboratory. 41 p.

 

Li, C.; Shimamoto, S. 2011. A Real Time Traffic Light Control Scheme for Reducing VehiclesCO2 Emissions. In Procedeeings of the 8th Annual IEEE Consumer Communications and Networking Conference - Emerging and Innovative Consumer Technologies and Applications. DOI: http://dx.doi.org/10.1109/CCNC.2011.5766627, 855-859.

 

Li, D.; Wong, D.K.; Sayeed, A.M. 2002. Detection, Classification and Tracking of Targets in Distributed Sensor Networks, IEEE Signal Processing Magazine. DOI: http://dx.doi.org/10.1109/79.985674, 19(2): 17-29.

 

Lilly, J.G. 2005. Engine Exhaust Noise Control. Available from Internet: .

 

Liu, L. 1999. Ground vehicle acoustic signal processing based on biological hearing models. M.Sc. thesis, Maryland University College Park Inst for Systems Research. 89 p.

 

Lo, K.W.; Ferguson, B.G. 2000. Broadband passive acoustic technique for target motion parameter estimation, IEEE Transactions on Aerospace and Electronic Systems. DOI: http://dx.doi.org/10.1109/7.826319, 36(1): 163-175.

 

Lopez, J.E.; Chen, H.H.; Saulnier, J. 1999. Target Identification Using Wavelet-based Feature Extraction and Neural Network Classifiers, Cytel Systems Inc., Hudson MA.

 

Mohan, P.; Padmanabhan, V.N.; Ramjee, R. 2008. Nericell: Rich monitoring of road and traffic conditions using mobile smartphone. In Proceedings of the 6th ACM conference on Embedded network sensor systems. DOI: http://dx.doi.org/10.1145/1460412.1460444, 323-336.

 

Nooralahiyan, A.Y.; Kirby, H.R . 1998. Vehicle Classification by Acoustic Signature, Mathematical and Computer Modelling. DOI: http://dx.doi.org/10.1016/S0895-7177(98)00060-0, 27(9-11): 205-214.

 

Quinn, B.G. 1996. Doppler speed and range estimation using frequency and amplitude estimates, Acoustical Society of America. DOI: http://dx.doi.org/10.1121/1.413221, 98(5): 2560-2566.

 

Road Directorate-Ministry of Transport. 2005. Noise Reducing Pavement, Road Directorate, Danish Road Institute Tech Report 141.

 

Roberts, W.J.J.; Sabrin, H.W.; Ephraim, Y. 2001. Ground Vehicle Classification using Hidden Markov Models, Atlantic coast technologies Inc., Silver Spring MD. 11 p.

 

Robertson, D.I.; Bretherton, R.D. 1991. Optimizing networks of traffic signals in real time-The SCOOT method, IEEE Transaction on Vehicular Technology. DOI: http://dx.doi.org/10.1109/25.69966, 40(1): 11-15.

 

Sampan, S. 1997. Neural fuzzy techniques in vehicle acoustic signal classification, Ph.D. dissertation, Department of Electrical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA. 187 p.

 

Sandberg, U. 2001. Tyre/Road Noise-Myths and Realities. In Proceedings of the International Congress and Exhibition on Noise Control Engineering, Hague Netherlands. 22 p.

 

Sandberg, U.; Ejsmont, A.J. 2002. Tyre/Road Noise Reference Book. Kisa, Sweden: Infomex, SE-59040.

 

Sarigul-Klijn, N.; Dietz, D.; Karnopp, D.; Dummer, J. 2001. A computational Aeroacoustic Model for Near and Far Field Vehicle Noise predictions. The American Institute of Aeronautics and Astronautics Inc. 11 p.

 

Sen, R.; Raman, B.; Sharma, P. 2010. Horn-ok-please. In Proceedings of the ACM MobiSys, San Francisco, C. 137-150.

 

Succi, G.; Pedersen, T.K.; Gampert, R.; Prado, G. 1999. Acoustic Target Tracking and Target Identification - Recent Results. In Proceedings of the Conference on Unattended Ground Sensor Technologies and Applications. DOI: http://dx.doi.org/10.1117/12.357130, 3713: 10-21.

 

Tyagi, V.; Kalyanaraman, S.; Krishnapuram, R. 2012. Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics, IEEE Transactions on Intelligent Transportation Systems. DOI: http://dx.doi.org/10.1109/TITS.2012.2190509, 13(3): 1156-1166.

 

Valcarce, R.; Mosquera, C.; Gonzalez, R. 2004. Estimation of road vehicle speed using two omnidirectional microphones: A maximum likelihood approach, EURASIP Journal on Advances in Signal Processing. DOI: http://dx.doi.org/10.1155/S1110865704311133, 2004: 1059-1077.

 

Wang, X.; Qi, H. 2002. Acoustic target classification using distributed sensor arrays. In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. DOI: http://dx.doi.org/10.1109/ICASSP.2002.5745661, 4: 4186-4189.

 

Wellman, M.C.; Srour, N.; Hills, D.B. 1997. Acoustic feature extraction for a neural network classifier Army Research Laboratory, Army Research Lab Adelphi Md. 25 p.

 

William, P.E.; Hoffman, M.W. 2011. Classification of Military Ground Vehicles Using Time Domain Harmonics’ Amplitudes, IEEE transactions on instrumentation and measurement. DOI: http://dx.doi.org/10.1109/TIM.2011.2135110, 60(11): 3720-3731.

 

Wu, H.; Mendel, J. 2007. Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers, IEEE Transaction on fuzzy systems. DOI: http://dx.doi.org/10.1109/TFUZZ.2006.889760, 15(1): 56-72.