The paper presents a system that recognizes the make, colour and type of the vehicle. The classification has been performed using low quality data from real-traffic measurement devices. For detecting vehicles’ specific features three methods have been developed. They employ several image and signal recognition techniques, e.g. Mamdani Fuzzy Inference System for colour recognition or Scale Invariant Features Transform for make identification. The obtained results are very promising, especially because only on-site equipment, not dedicated for such application, has been employed. In case of car type, the proposed system has better performance than commonly used inductive loops. Extensive information about the vehicle can be used in many fields of Intelligent Transport Systems, especially for traffic supervision.
Du S, Ibrahim M, Shehata MS, Badawy WM. Automatic license plate recognition (alpr): A state-of-theart review. IEEE Trans. Circuits Syst. Video Techn. 2013;23(2): 311-325.
Wang F, Man L, Wang B, Xiao Y, Pan W, Lu X. Fuzzybased algorithm for colour recognition of license plates. Pattern Recognition Letters. 2008;29(7): 1007-1020.
Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (surf). Comput. Vis. Image Underst. 2008;110: 346-359.
Pearce G, Pears N. Automatic make and model recognition from frontal images of cars. 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 30 Aug.-2 Sept. 2011, Klagenfurt, Austria. IEEE; 2011. p. 373-378.
Gupta P, Purohit GN Saroj K. Vehicle color recognition system using CCTV cameras. International Journal of Advanced Research in Computer Science and Software Engineering. 2014;4(1): 383-389.
Baran R, Glowacz A, Matiolanski A. The efficient realand non-real-time make and model recognition of cars. Multimedia Tools and Applications. 2015;74(12): 4269–4288.
Gu H-Z, Lee S-Y. Car model recognition by utilizing symmetric property to overcome severe pose variation. Machine Vision and Applications. 2013;24(2): 255-274.
Butzke M, Silva AG, Hounsell M, Pillon MA. Automatic recognition of vehicle attributes: color classification and logo segmentation. Hifen. 2008;32(62): 293-300.
Chen Z, Pears N, Freeman M, Austin J. Road vehicle classification using support vector machines. IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, 20-22 Nov. 2009, Shanghai, China. IEEE; 2009. p. 214-218.
Zhan W, Wan Q. Real-time and automatic vehicle type recognition system design and its application. In: Li Z, Li X, Liu Y, Cai Z. (eds.) Computational Intelligence and Intelligent Systems. Communications in Computer and Information Science, Vol. 316. Berlin, Heidelberg: Springer; 2012. p. 200-208.
COST. TUD COST Action 323. Weighing in motion of road vehicles; 1999.
Federal Highway Administration. Available from: http://www.fhwa.dot.gov.
Negri P, Clady X, Milgram M, Poulenard R. An oriented-contour point based voting algorithm for vehicle type classification. 18th International Conference on Pattern Recognition, ICPR 2006, 20-24 Aug. 2006, Hong Kong, China. IEEE; 2006. p. 574-577.
Munroe DT, Madden MG. Multi-class and single-class classification approaches to vehicle model recognition from images. AICS; 2005.
Petrovic V, Cootes TF. Analysis of features for rigid structure vehicle type recognition. British Machine Vision Conference, 7-9 Sep. 2004, London, U.K.; 2004. p. 587-596.
Kazemi FM, Samadi S, Poorreza HR, Akbarzadeh-Totonchi MR. Vehicle recognition using curvelet transform and SVM. Forth International Conference on Information Technology, ITNG’07, 2-4 Apr. 2007, Las Vegas, NV, USA. IEEE; 2007. p. 516-521.
Iqbal U, Zamir SW, Shahid MH, Parwaiz K, Yasin M, Sarfraz M. Image based vehicle type identification. 2010 International Conference on Information and Emerging Technologies (ICIET), 14-16 June 2010, Harachi, Pakistan. IEEE; 2010. p. 1-5.
Vapnik VN. The nature of statistical learning theory. New York: Springer-Verlag; 1995.
Bernardo JM, Smith AFM. Bayesian Theory. John Wiley & Sons; 2008. p. 587-595.
Lee H. Neural network approach to identify model of vehicles. In: Wang J, Yi Z, Zurada JM, Lu BL, Yin H. (eds.) Advances in Neural Networks - ISNN 2006. Lecture Notes in Computer Science, Vol. 3973. Berlin, Heidelberg: Springer; 2006. p. 66-72.
Abdelmaseeh M, Badreldin I, Abdelkader M, El Saban M. Car make and model recognition combining global and local cues. 21st International Conference on Pattern Recognition (ICPR 2012), 11-15 Nov. 2012, Tsukuba, Japan; 2012. p. 910-913.
Wu Y-T, Kao J-H, Shih M-Y. A vehicle color classification method for video surveillance system concerning model-based background subtraction. Proceedings of the 11th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing – PCM 2010, 21-24 Sep. 2010, Shanghai, China; 2010. p. 369-380.
Baek N, Park S-M, Kim K-J, Park S-B. Vehicle color classification based on the support vector machine method. In: Huang DS, Heutte L, Loog M. (eds.) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007, Communications in Computer and Information Science, vol. 2; Berlin, Heidelberg: Springer; 2007. p. 1133-1139.
Kim K-J, Park S-M, Choi Y-J. Deciding the number of color histogram bins for vehicle color recognition. IEEE Asia-Pacific Services Computing Conference (APSCC ’08), 9-12 Dec. 2008, Yilan, Taiwan. IEEE; 2008. p. 134-138.
Son J-W, Park S-B, Kim K-J. A convolution kernel method for color recognition. Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007), 22-24 Aug. 2007, Luoyang, Henan, China. IEEE; 2007. p. 242-247.
Badura P, Skotnicka M. Automatic car make recognition in low-quality images. In: Piętka E, Kawa J, Wieclawek W. (eds.) Information Technologies in Biomedicine, Volume 3. Advances in Intelligent Systems and Computing, vol. 283. Cham, Switzerland: Springer; 2014. p. 235-246.
Wieclawek W, Pietka E. Car Segmentation and Color Recognition. Proceedings of the 21st International Conference: Mixed Design of Integrated Circuits Systems (MIXDES), 19-21 June 2014, Lublin, Poland.
IEEE; 2014. p. 426-429.
Lowe DG. Object recognition from local scale-invariant features. The Proceedings of the Seventh IEEE International Conference on Computer Vision, 20-27 Sep. 1999, Kerkyra, Greece. IEEE; 1999. p. 1150-1157.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision. 2004;60: 91-110.
Lindeberg T. Scale-space theory: A basic tool for analysing structures at differentscales. Journal of Applied Statistics. 1994;21(2): 224-270.
Lindeberg T. Scale-Space Theory in Computer Vision. Norwell: Kluwer Academic Publishers; 1994.
Badura S, Foltan S. Advanced scale-space, invariant, low detailed feature recognition from images - car brand recognition. Proceedings of the 2010 International Multiconference on Computer Science and Information Technology (IMCSIT), 18-20 Oct 2010, Wisla, Poland. IEEE; 2010. p. 19-23.
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