A robust traffic quantity measurement with video surveillance
Document Type
Article
Publication Date
1-1-2011
Abstract
Video and image processing have been used for traffic supervision, analysis and monitoring of traffic condition in many cities and urban areas. The system described in this paper aims to approach the precise method to obtain the traffic flow, time headway and traffic volume through a sequence of images captured with a stationary video camera. The method consists of three algorithms. First, background modeling and update, second, a boosting method to enhance the foreground image and reduce the noise and at last determining best match of region of interest (ROI) to extract information to conclude if there is a vehicle in the detection zone or not. Based on this structure, the traffic quantity measurement (TQM) algorithm is represented to compute the important parameters in traffic sense that will be useful for traffic condition observation and management as well. In this research, the traffic quantities such as time headway and traffic flow have been measured. The experimental result shows this method obtains traffic flow and time headway with around 91 of accuracy in shadow free area and can be used in real time condition.
Keywords
Computer vision, Traffic flow, Vehicle time headway
Divisions
fac_eng
Publication Title
International Journal of Physical Sciences
Volume
6
Issue
5
Additional Information
Cited By (since 1996):1 Export Date: 16 December 2013 Source: Scopus Language of Original Document: English Correspondence Address: Rad, A. G.; Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; email: Hsara56@perdana.um.edu References: Asaad, A.M., Syed, I.A., (2009) Object Identification In Video Images Using Morphological Background Estimation Scheme, 22, pp. 279-288; Bailo, G., Bariani, M., Ijas, P., Raggio, M., Background estimation with Gaussian distribution for image segmentation, a fast approach (2005) Proc. IEEE Int workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal safety, pp. 2-5; Coifman, B., Beymer, D., McLauchlan, P., Malik, J., A real-time computer vision system for vehicle tracking and traffic surveillance (1998) Transp. Res. Record Part C, 6, pp. 271-288; Hsu, W.L., Liao, H.Y.M., Jeng, B., Fan, K., Traffic parameter extraction using entropy measurements (2002) Proc., IEEE 5th Int. Conf. Intell. Transp. Syst, pp. 106-111; Lee, D.H., Park, Y.T., Robust vehicle detection based on shadow classification (2006) Proc. 18th Conf. On Pattern Recognition, 3, pp. 1167-1170; Rad, A.G., Abbas, D., Mohamed, R.K., Vehicle speed detection in video image sequences using CVS method (2010) Int. J. Phys. Sci, 5 (17), pp. 2555-2563; Ridder, C., Munkel, O., Kirchner, H., (1995) Adaptive Background Estimation and Foreground Detection Using Kalman-filter, , Technical report, Bavarian Research Center for Knowledge-Based Systems; Ronse, C., Laurent, N., Etienne, D., (2005) Mathematical Morphology: 40 Years On: Proceedings of the 7th International Symposium On Mathematical Morphology, , April 18-20, (Computational Imaging and Vision) ISBN 1-4020-3442-3 (2005); Rosenfeld, A., Connectivity in Digital Pictures (1970) J. ACM (JACM), pp. 146-160; Stauffer, C., Grimson, W., Adaptive background mixture models for real-time tracking (1999) Proc. IEEE Conference On Computer Vision and Pattern Recognition. FortCollins. Colorado, pp. 246-252; Shi, P., Jones, E.G., Zhu, Q., Adaptive Median Filter for Background Generation - A Heuristic Approach (2002) Proceed. Int. Conf. Imaging Sci. Syst. Technol., CISST '02: 1, CSREA Press, pp. 173-179; Sonka, M., Vaclav, H., Roger, B., (1999) Image Processing, Analysis, and Machine Vision, pp. 370-375. , Second. Edition. PWS Publishing; Viarani, E., Extraction of traffic information from image at DEIS," Proceedings (1999) Int. Conf. Image Anal. Processing, pp. 1073-1076; Wu, G.K., Reed, T.R., Image sequence processing using spatiotemporal segmentation (1999) Circuits and Systems For Video Technology, IEEE Trans, pp. 798-807; Zhu, Z., Xu, G., Yang, B., Shi, D., Lin, X., (2000) A Real-time Vision System For Automatic Traffic Monitoring Image, pp. 781-794