Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32759
Design and Implementation of a Counting and Differentiation System for Vehicles through Video Processing

Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

Abstract:

This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: Intelligent transportation systems, object detection, video processing, road traffic, vehicle counting, vehicle classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126830

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1555

References:


[1] Y. Benezeth, P.-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, “Comparative study of background subtraction algorithms,” Journal of Electronic Imaging, vol. 19, no. 3, pp. 033 003–033 003, 2010.
[2] A. Sobral and A. Vacavant, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,” Computer Vision and Image Understanding, vol. 122, pp. 4–21, 2014.
[3] A. Sanin, C. Sanderson, and B. C. Lovell, “Shadow detection: A survey and comparative evaluation of recent methods,” Pattern recognition, vol. 45, no. 4, pp. 1684–1695, 2012.
[4] Z. Chen, T. Ellis, S. Velastin et al., “Vehicle detection, tracking and classification in urban traffic,” in Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, 2012, pp. 951–956.
[5] N. C. Mithun, N. U. Rashid, and S. Rahman, “Detection and classification of vehicles from video using multiple time-spatial images,” Intelligent Transportation Systems, IEEE Transactions on, vol. 13, no. 3, pp. 1215–1225, 2012.
[6] L. Unzueta, M. Nieto, A. Cort´es, J. Barandiaran, O. Otaegui, and P. S´anchez, “Adaptive multicue background subtraction for robust vehicle counting and classification,” Intelligent Transportation Systems, IEEE Transactions on, vol. 13, no. 2, pp. 527–540, 2012.
[7] J.-C. Lai, S.-S. Huang, and C.-C. Tseng, “Image-based vehicle tracking and classification on the highway,” in Green Circuits and Systems (ICGCS), 2010 International Conference on. IEEE, 2010, pp. 666–670.
[8] A. S´anchez, E. Nunes, and A. Conci, “Using adaptive background subtraction into a multi-level model for traffic surveillance,” Integrated Computer-Aided Engineering, vol. 19, no. 3, pp. 239–256, 2012.
[9] Z. Zivkovic, “Improved adaptive gaussian mixture model for background subtraction,” in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2. IEEE, 2004, pp. 28–31.
[10] C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., vol. 2. IEEE, 1999.
[11] J.-B. Huang and C.-S. Chen, “Moving cast shadow detection using physics-based features,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009, pp. 2310–2317.
[12] J. Canny, “A computational approach to edge detection,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, no. 6, pp. 679–698, 1986.
[13] S. Suzuki et al., “Topological structural analysis of digitized binary images by border following,” Computer Vision, Graphics, and Image Processing, vol. 30, no. 1, pp. 32–46, 1985.
[14] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Fluids Engineering, vol. 82, no. 1, pp. 35–45, 1960.
[15] H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.