Traffic monitoring is important in every country to cope up with the increasing population. Tracking and vehicle monitoring is always a challenging task as they are used for surveillance control and traffic planning. In earlier method, the detection of vehicles is classified using Artificial Neural Network with Histograms of Oriented Gradients. The major challenge due to advent of computer is to choose appropriate algorithms for real time dataset. Therefore, the entire work in this study is carried out by using Python with OpenCV method. A vehicle detection and classification algorithm that works in real time is proposed in this work. Further, the heavy vehicles detection is classified using the Support Vector Machine with a new set of features, Histograms of Oriented Gradients. The results show that the proposed method with Support Vector Machine training parameters is better than earlier method.

Vehicle detection, Classification, Python, OpenCV, Support Vector Machine, Histograms of Oriented Gradients, Traffic Surveillance

[1] N. Buch, S. A. Velastin, and J. Orwell, “A review of computer vision techniques for the analysis of urban traffic,” IEEE Trans. on IntelligentTransportation Systems, vol. 12, no. 3, pp. 920–939, 2011.
[2] G. Wang, D. Xiao, and J. Gu, “Review on vehicle detection based on video for traffic surveillance,” IEEE Int. Conf. on Automation and Logistics, pp. 2961–2966, 2008.
[3A.I. Awad and M. Hassaballah (eds.), Image Feature Detectors and Descriptors,Studies in Computational Intelligence.Springer 2016.
[4] K. B. Saran and G. Sreelekha, Traffic video surveillance: Vehicle detection and Classification. In International Conference on Control Communication & computing India (ICCC) IEEE, pp.516-521, November 2015
[5] B. Morris and M. Trivedi, “Robust classification and tracking of vehicles in traffic video streams,” Intelligent Transportation Systems Conference,ITSC’06, p. 10781083, 2006.
[6] ] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893, June 2005.
[7] Michael Beyeler Machine Learning for OpenCV, OpenCV library function. Retrived October 12, 2011
[8] David Beazley and Brian K. Jones, Python cookbook, 3rd edition,2013
[9] R. Wang, L. Zhang, K. Xiao, R. Sun, and L. Cui, “Easisee: Real-time vehicle classification and counting via low-cost collaborative sensing,” IEEE Trans. on Intelligent Transportation Systems, vol. 15, pp. 414–424, February 2014.
[10] Berkant Başa Implementation of hog edge detection algorithm onfpga’s, Procedia - Social and Behavioral Sciences 174 ( 2015 ) 1567 – 1575.
[11] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” Computer vision-ECCV 2006, pp. 404–417, 2006.
[12] G. Wang, D. Xiao, and J. Gu, “Review on vehicle detection based on video for traffic surveillance,” IEEE Int. Conf. on Automation and Logistics, vol. 2961, pp. 1–3, September 2008.
[13] M. F. Zezhi Chen, Nick Pears and J. Austin, “A gaussian mixture model and support vector machine approach to vehicle type and colour classification,” IET Intelligent Transport Systems, February 2012.
[14] B. Morris and M. Trivedi, “Improved vehicle classification in long traffic video by cooperating tracker and classifier modules,” IEEE Int. Conf. on Video and Signal Based Surveillance, vol. 9, p. 9, November 2006.
[15] M. Daigavane, and P. R. Bajaj, “Real Time Vehicle Detection and Counting Method for Unsupervised Traffic Video on Highways,” International Journal of Computer Science and Network Security, vol.10, no. 8, pp. 112-117, 2014.
[16] A. Ambardekar and M. Nicolescu, “Vehicle classification framework: a comparative study,” EURASIP Journal on Image and Video Processing,
January 2014.
[17] S Aslani, H Mahdavi-Nasab Optical flow based moving object detection and tracking for traffic surveillance” In the proceedings of World Academy of Science, Engineering and Technology International Journal of Electrical and Computer Engineering Vol:7, No:9, 2013
[18] Yong Tang, Congzhe Zhang, Renshu Gu, Peng Li, Bin Yang, “Vehicle detection and recognition for intelligent traffic surveillance system” In the proceedings of Multimedia tools and Applications, Springer, February 2017, Volume 76, Issue 4, pp 5817–5832 [19]Mahdi Moghimi, Mohammad; Nayeri, Maryam; Pourahmadi, Majid; Kazem Moghimi, Mohammad, Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos International Journal of Imaging and Robotics, vol. 18, no. 1, pp. 94-106 (2018)