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

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 monitoring 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.

[1]  Ulf Sandberg,et al.  Tyre/road noise reference book , 2002 .

[2]  Akbar M. Sayeed,et al.  Detection, Classification and Tracking of Targets in Distributed Sensor Networks , 2002 .

[3]  Somkiat Sampan,et al.  Neural Fuzzy Techniques In Vehicle Acoustic Signal Classification , 1997 .

[4]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[5]  Grant R. Gerhart,et al.  Wavelet-based ground vehicle recognition using acoustic signals , 1996, Defense + Commercial Sensing.

[6]  Brian G. Ferguson,et al.  Broadband passive acoustic technique for target motion parameter estimation , 2000, IEEE Trans. Aerosp. Electron. Syst..

[7]  Hung Han Chen,et al.  Target Identification Using Wavelet-based Feature Extraction and Neural Network Classifiers , 1999 .

[8]  Sen M. Kuo,et al.  Active noise control: a tutorial review , 1999, Proc. IEEE.

[9]  Katsushi Ikeuchi,et al.  Traffic monitoring and accident detection at intersections , 2000, IEEE Trans. Intell. Transp. Syst..

[10]  B. Quinn Doppler speed and range estimation using frequency and amplitude estimates , 1995 .

[11]  R. Graf,et al.  On the horn effect of a tyre/road interface, part I: experiment and computation , 2002 .

[12]  Wr Graham,et al.  ON THE HORN EFFECT OF A TYRE/ROAD INTERFACE, PART II: ASYMPTOTIC THEORIES , 2002 .

[13]  R. D. Bretherton,et al.  Optimizing networks of traffic signals in real time-the SCOOT method , 1991 .

[14]  J. Hunt,et al.  Highway Modeling. Part I: Prediction of Velocity and Turbulence Fields in the Wake of Vehicles , 1979 .

[15]  Shiping Chen,et al.  Traffic monitoring using digital sound field mapping , 2001, IEEE Trans. Veh. Technol..

[16]  Nassy Srour,et al.  Acoustic Feature Extraction for a Neural Network Classifier. , 1997 .

[17]  Gervasio Prado,et al.  Acoustic target tracking and target identification: recent results , 1999, Defense, Security, and Sensing.

[18]  Manohar Das,et al.  An efficient technique for modeling and synthesis of automotive engine sounds , 2001, IEEE Trans. Ind. Electron..

[19]  Denis McKeown,et al.  Vehicle classification by acoustic signature , 1998 .

[20]  Jerry M. Mendel,et al.  Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers , 2007, IEEE Transactions on Fuzzy Systems.

[21]  Fernando Pérez-González,et al.  Estimation of Road Vehicle Speed Using Two Omnidirectional Microphones: A Maximum Likelihood Approach , 2004, EURASIP J. Adv. Signal Process..

[22]  Basavaraj S. Anami,et al.  Comparative performance analysis of three classifiers for acoustic signal-based recognition of motorcycles using time- and frequency-domain features , 2012 .

[23]  Volkan Cevher,et al.  Vehicle Speed Estimation Using Acoustic Wave Patterns , 2009, IEEE Transactions on Signal Processing.

[24]  Vincent Mirelli,et al.  Distributed acoustic sensor data processing for target classification , 2006, SPIE Defense + Commercial Sensing.

[25]  Peter E. William,et al.  Classification of Military Ground Vehicles Using Time Domain Harmonics' Amplitudes , 2011, IEEE Transactions on Instrumentation and Measurement.

[26]  Li Liu Ground Vehicle Acoustic Signal Processing Based on Biological Hearing Models , 1999 .

[27]  Hairong Qi,et al.  Acoustic target classification using distributed sensor arrays , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[28]  Shivkumar Kalyanaraman,et al.  Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics , 2012, IEEE Transactions on Intelligent Transportation Systems.

[29]  David Beymer,et al.  A real-time computer vision system for vehicle tracking and traffic surveillance , 1998 .

[30]  Shigeru Shimamoto,et al.  A real time traffic light control scheme for reducing vehicles CO2 emissions , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[31]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[32]  Yoram Bresler,et al.  Doppler-based motion estimation for wide-band sources from single passive sensor measurements , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[33]  Douglas Lake,et al.  Efficient Maximum Likelihood Estimation for Multiple and Coupled Harmonics , 1999 .

[34]  Paola Mello,et al.  Image analysis and rule-based reasoning for a traffic monitoring system , 2000, IEEE Trans. Intell. Transp. Syst..

[35]  B. Bridge,et al.  Automatic traffic monitoring by intelligent sound detection , 1999 .

[36]  S. Chen,et al.  Automatic traffic monitoring by intelligent sound detection , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[37]  Erich J. Plate,et al.  Modelling of vehicle-induced turbulence in air pollution studies for streets , 2000 .

[38]  J. Kato An attempt to acquire traffic density by using road traffic sound , 2005, Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005)..

[39]  Volkan Cevher,et al.  Joint Acoustic-Video Fingerprinting of Vehicles, Part I , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[40]  Ulf Sandberg,et al.  Tyre/road noise : myths and realities , 2001 .

[41]  Paola Mello,et al.  Image analysis and rule-based reasoning for a traffic monitoring system , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[42]  Bhaskaran Raman,et al.  Horn-ok-please , 2010, MobiSys '10.

[43]  Nesrin Sarigul-Klijn,et al.  A computational aeroacoustic method for near and far field vehicle noise predictions , 2001 .