Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
Reseach Article

Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model

by Khalid Housni, Driss Mammass, Youssef Chahir
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 10
Year of Publication: 2012
Authors: Khalid Housni, Driss Mammass, Youssef Chahir
10.5120/5001-7283

Khalid Housni, Driss Mammass, Youssef Chahir . Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model. International Journal of Computer Applications. 40, 10 ( February 2012), 20-27. DOI=10.5120/5001-7283

@article{ 10.5120/5001-7283,
author = { Khalid Housni, Driss Mammass, Youssef Chahir },
title = { Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 10 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number10/5001-7283/ },
doi = { 10.5120/5001-7283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:43.110903+05:30
%A Khalid Housni
%A Driss Mammass
%A Youssef Chahir
%T Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 10
%P 20-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The tracking of moving objects in a video sequence is an important task in different domains such as video compression, video surveillance and object recognition. In this paper, we propose an approach for integrated tracking and segmentation of moving objects from image sequences where the camera is in movement. This approach is based on the calculation of minimal cost of a cut in a graph “Graph Cuts” and the 2D parametric motion models estimated between successive images. The algorithm takes advantage of smooth optical flow which is modeled by affine motion and graph cuts in order to reach maximum precision and overcome inherent problems of conventional optical flow algorithms. Our method is simple to implement and effective. Experimental results show the good performance and robustness of the proposed approach.

References
  1. D. Terzopoulos, R. Szeliski. 1993. Tracking with kalman snakes. Active vision, pp.3–20.
  2. M. Isard, A. Blake. 1998. Condensation – conditional density propagation for visual tracking. Int. J. Computer Vision, 29(1) :5–28.
  3. J. MacCormick, A. Blake. 2000. A probabilistic exclusion principle for tracking multiple objects. Int. J. Computer Vision, 39(1) :57–71.
  4. C.R. Wren, A. Azarbayejani, T. Darrell, A.P. Pentland. 1997. P?nder: real-time tracking of the human body. IEEE Trans. Pattern Anal.Mach. Intell. 19(7), 780–785.
  5. N. Xu, N. Ahuja. 2002. Object contour tracking using graph cuts based active contours. Proc. Int. Conf. Image Processing.
  6. B. Rosenhahn, U. Kersting, S. Andrew, T. Brox, R. Klette and H-P. Seidel. 2005. A silhouette based human motion tracking system Technical Report (The University of Auckland, New Zealand: CITR) ISSN: 1178-3581.
  7. B. K..P. Horn, B. G. Schunck. 1981. Determining Optical Flow. AI 17, pp. 185—203.
  8. J. Barron, D. Fleet, S. Beauchemin. 1994. Performance of optical ?ow techniques. Int. J. of Comput. Vis. 12(1), 43–77.
  9. A.R. Mansouri, J. Konrad. 2003. Multiple motion segmentation with level sets. IEEE Trans. Image Process. 12(2), 201–220.
  10. G. Adiv. 1985. Determining 3D motion and structure from optical ?ow generated by several moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 7(4), 384–401.
  11. A. Yilmaz, O. Javed, M. Shah. 2006. Object tracking: A survey. ACM Comput.Surv. 38, 13.
  12. C. Veenman, M. Reinders, and E. Backer. 2001. Resolving motion correspondence for densely moving points. IEEE Trans. Patt. Analy. Mach. Intell. 23, 1, 54–72.
  13. D. Serby, S. Koller-Meier, and L. V. Gool. 2004. Probabilistic object tracking using multiple features. In IEEE International Conference of Pattern Recognition (ICPR). 184–187.
  14. D. Comaniciu, V. Ramesh and P. Meer. 2003. Kernel-based object tracking. IEEE Trans. Patt. Analy.Mach. Intell. 25, 564–575.
  15. Y. Boykov, O. Veksler, and R. Zabih. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222–1239.
  16. Y. Boykov, M.P. July. 2001. Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. In: proceedings of the International Conference on Computer Vision, Vancouver Canada.
  17. K. Housni, D. Mammass and Y. Chahir. 2009. Interactive ROI Segmentation using Graph Cuts, GVIP-ICGST Journal,Volume 9, Issue 6, pp 1—6..
  18. D. Greig, B. Porteous, and A. Seheult. 1989. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, Series B, 51(2):271–279.
  19. V. Kwatra, A. Schodl, I. Essa, and A. Bobick. 2003. Graphcut textures: image and video synthesis using graph cuts. In Proceedings of SIGGRAPH.
  20. V. Kolmogorov. 2004. What Energy Functions Can Be Minimized via Graph Cuts?. IEEE transactions on pattern analysis and machine intelligence, vol. 26, NO. 2.
  21. L. Ford and D. Fulkerson. 1962. Flows in Networks. Princeton University Press.
  22. A. Goldberg and R. Tarjan October. 1988. A new approach to the maximum flow problem. Journal of the Association for Computing Machinery, 35(4):921–940.
  23. Y. Boykov and V. Kolmogorov. 2000. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. 3rd. International Workshop on EMMCVPR. Springer-Verla, September.
  24. J.-M. Odobez, P. Bouthemy. 1995. Robust multiresolution estimation of parametric motion models. Journal of Visual Communication and Image Representation, 6(4):348-365.
  25. Vista team, http://www.irisa.fr/vista
Index Terms

Computer Science
Information Sciences

Keywords

Graph Cuts Motion Models Tracking Video Analysis Overlapping Objects.