We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects

by Ekta Patel, Dolley Shukla
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 5
Year of Publication: 2013
Authors: Ekta Patel, Dolley Shukla
10.5120/10465-5180

Ekta Patel, Dolley Shukla . Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects. International Journal of Computer Applications. 63, 5 ( February 2013), 32-37. DOI=10.5120/10465-5180

@article{ 10.5120/10465-5180,
author = { Ekta Patel, Dolley Shukla },
title = { Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 5 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number5/10465-5180/ },
doi = { 10.5120/10465-5180 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:13:23.294831+05:30
%A Ekta Patel
%A Dolley Shukla
%T Comparison of Optical Flow Algorithms for Speed Determination of Moving Objects
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 5
%P 32-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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
  1. A. M. Tekalp, E. F. (1995), "Digital Video Processing. Englewood Cliffs", NJ: Prentice-Hall, 1995.
  2. "Different Approaches for Motion Estimation", E. F. ( 4th-6th June 2009 ), International Conference on control, automation, communication and energy conservation -2009.
  3. Chi-Cheng Cheng, and Hui-Ting Li . E. F. ( 2006), "Feature-Based Optical Flow Computation", International Journal of Information Technology,Vol. 12,pp. 7.
  4. 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.
  5. 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.
  6. S. Baker, I. Matthews, E. F. ( March 2004 ), "Lucas-Kanade 20 Years On: A Unifying Framework", IJCV, Vol. 56, No. 3, ,pp. 221-255.
  7. B. K. P. Horn, B. G. Schunck, E. F. (1981),"Determining Optical Flow", Artificial Intelligence, Vol. 2, pp. 185-203.
  8. 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.
  9. 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.
  10. 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.
  11. Bruhn, J. Weickert, C. Feddern, T. Kohlberger, C. Schnörr, E. F. (2003 - 2005 ), "Towards ultimate motion estimation: Combining highest accuracy with real-time performance".
  12. 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.
  13. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Tracking Optical flow Motion estimation Lucas-Kanade algorithm

Powered by PhDFocusTM