Abstract

In this paper, we present a semi real-time vehicle tracking algorithm to determine the speed of the vehicles in traffic from traffic cam video. The results of this work can be used for traffic control, security and safety both by government agencies and commercial organizations. In this paper a method is described for tracking moving objects from a sequence of video frame. This method is implemented by using optical flow (Horn-Schunck)and (Lucas-Kanade) in mat lab and Simulink. It has a variety of uses, some of which are: human computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Segmentation is performed to detect the object after reducing the noise from that scene. The object is tracked by plotting a rectangular bounding box around it in each frame. The velocity of the object is determined by calculating the distance that the object moved in a sequence of frames with respect to the frame rate that the video is recorded. Comparison and performance analysis of algorithms based on psnr and average angular error is done.

References

• A. M. Tekalp, E. F. (1995), "Digital Video Processing. Englewood Cliffs", NJ: Prentice-Hall, 1995.
• "Different Approaches for Motion Estimation", E. F. ( 4th-6th June 2009 ), International Conference on control, automation, communication and energy conservation -2009.
• Chi-Cheng Cheng, and Hui-Ting Li . E. F. ( 2006), "Feature-Based Optical Flow Computation", International Journal of Information Technology,Vol. 12,pp. 7.
• Savan Chhaniyara, Pished Bunnun, Lakmal D. Seneviratne and Kaspar Althoefer, E. F. ( MARCH 2008), "Optical Flow Algorithm for Velocity Estimation of Ground Vehicles: A Feasibility Study", International Journal on smart sensing and intelligent systems, VOL. 1, pp. no. 1.
• E. Atko?ci ¯unas, R. Blake, A. Juozapavi?cius, M. Kazimianec. , E. F. ( 2005), "Image Processing in Road Traffic Analysis'. Nonlinear Analysis: Modelling and Control", Vol. 10, No. 4, 315–332.
• S. Baker, I. Matthews, E. F. ( March 2004 ), "Lucas-Kanade 20 Years On: A Unifying Framework", IJCV, Vol. 56, No. 3, ,pp. 221-255.
• B. K. P. Horn, B. G. Schunck, E. F. (1981),"Determining Optical Flow", Artificial Intelligence, Vol. 2, pp. 185-203.
• P. Subashini, M. Krishnaveni, Vijay Singh, E. F. (August 2011), "Implementation of Object Tracking System Using Region Filtering Algorithm based on Simulink Block sets", International Journal of Engineering Science and Technology(IJEST),Vol. 3 No. 8. PP-6744-6750. ISSN:0975-5462.
• J. L. Barron, D. J. Fleet, and S. S. Beauchemin, E. F. ( February 1994 ), "Performance of Optical Flow Techniques", International Journal of Computer Vision, , vol. 12(1), pp. 43-77.
• Bruhn, J. Weickert and C. Schnörr , E. F. (2005), "Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods", International Journal of Computer Vision, 61(3): 211–231.
• Bruhn, J. Weickert, C. Feddern, T. Kohlberger, C. Schnörr, E. F. (2003 - 2005 ), "Towards ultimate motion estimation: Combining highest accuracy with real-time performance".
• Lazaros Grammatikopoulos, George Karras, Elli Petsa, E . F. ( November, 2005 ),"Automatic Estimation of Vehicle, Speed from Uncalibrated Video Sequences", International Symposium on Modern Technologies, Education & Professional Practice in Geodesy and related fields, Sofia,03 – 04.
• L. Li and Y. Yang:, E. F. (2010), "Optical flow estimation for a periodic image sequence",IEEE Trans. on Image Processing, 19(1): 1-10.