Vehicle Type Classification with Geometric and Appearance Attributes

Authors: Ghada S. Moussa

Abstract:

With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management.

This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: Appearance attributes, Geometric attributes, Support vector machine, Vehicle classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091468

References:

[1] Kafai, M. and Bhanu, B., Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions On Industrial Informatics, vol. 8, no. 1, February 2012.
[2] W.H. Lin, J. Dahlgren, and H. Huo, "An Enhancement to Speed Estimation Using Single Loop Detectors,” Proceeding of Intelligent Transportation Systems, 2003, pp. 417–422.
[3] H.J. Cho and M.T., "A Support Vector Machine Approach To CMOS-Based Radar Signal Processing For Vehicle Classification And Speed Estimation,” Mathematical and Computer Modelling, Volume 58, Issues 1–2, July 2013, Pages 438–448.
[4] E. Dallalzadeh, D.S. Guru, S. Manjunath and M. G.Suraj, "Classification of Moving Vehicles in Traffic Videos, Advances in Computer Science and Information Technology,” Computer Science and Engineering, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Volume 85, 2012, pp 211-221
[5] Z. Chen and T. Ellis, "Multi-shape Descriptor Vehicle Classification for Urban Traffic,” International Conference on Digital Image Computing: Techniques and Applications, pp. 456–461, Dec. 2011.
[6] R.P. Avery, Y. Wang, G.S. Rutherford, "Length-Based Vehicle Classification Using Images from Uncalibrated Video Cameras,” in: Proceedings of the 7th International IEEE Conference on Intelligent Transportation System, pp.737-742, 2004.
[7] G. Zhang, R.P. Avery, Y. Wang, "A Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras,” Transportation Research Record: Journal of the Transportation Research Board, 1993: 138-147, 2007.
[8] G. Moussa and K. Hussain, "Laser Intensity Automatic Vehicle Classification System,” North American Travel Monitoring Exposition and Conference (NATMEC)”, Washington, DC, USA, August 6-8, 2008.
[9] X. Ma, W. Eric, and L. Grimson, "Edge-Based Rich Representation for Vehicle Classification”, Proc. Int. Conf. Computer Vision, vol. 2, pp. 1185- 1192, 2005.
[10] L. Zhang, S.Z. Li, X. Yuan, S. Xiang, "Real-Time Object Classification in Video Surveillance Based On Appearance Learning,” in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007.
[11] B, Morris, M.M Trivedi, "Learning, Modelling and Classification of Vehicle Track Patterns from Live Video”, IEEE Trans on Intelligent Transport Systems, vol.9, no.3, pp. 425-437, 2008.
[12] G. Moussa, "A Reliable Multi-Type Vehicle Classification System,” submitted for publication.
[13] Buch, N.., Velastin, S. A. and Orwell J. 2011, A Review of Computer Vision Techniques for the Analysis of Urban Traffic, IEEE Transactions On Intelligent Transportation Systems, Vol. 12, No. 3, September 2011.
[14] Lowe, D.G. "Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, Vol. 60, No. 2. pp. 91-110-110., 2004.
[15] H. Bay, T. Tuytelaars, and L. Van Gool. SURF: Speeded up Robust Features. In ECCV, 2006.
[16] Csurka, G. Dance, C. R. Fan, L. Willamowski, J. and Bray C., "Visual Categorization with Bags of Keypoints,” in Proc. European Conference on Computer Vision (ECCV), May 2004
[17] Sivic, J. Russell, B. C Efros, A. A. Zisserman, A. and Freeman, W. T. "Discovering Objects and Their Location in Images,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV), vol. 1, October 2005, pp. 370–377.
[18] Creusen I.M., Wijnhoven R.G.J., and With P.H.N. de (2009). Applying Feature Selection Techniques for Visual Dictionary Creation in Object Classification. Proceedings of the 2009 International Conference on Image Processing, Computer Vision and Pattern Recognition, 13-16 July 2009, Las Vegas, Nevada, (2, pp. 722-727).
[19] G. J. McLachlan, K-A Do, C. Ambroise, Analyzing Microarray Gene Expression Data. Wiley, 2004.
[20] Matlab 2013, Computer Vision System Toolbox User’s Guide, The Math Works, Inc. http://www.mathworks.com/help/pdf_doc/ vision/vision_ug.pdf, last access January 2014.