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Object Tracking Using a New Level Set Model

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DOI: 10.4236/jsip.2014.51004    2,892 Downloads   4,323 Views   Citations

ABSTRACT

This paper proposes a novel implementation of the level set method that achieves real-time level-set-based object tracking. In the proposed algorithm, the evolution of the curve is realized by simple operations such as switching values of the level set functions and there is no need to solve any partial differential equations (PDEs). The object contour could change due to the change in the location, orientation or due to the changeable nature of the object shape itself. Knowing the contour, the average color value for the pixels within the contour could be found. The estimated object color and contour in one frame are the bases for locating the object in the consecutive one. The color is used to segment the object pixels and the estimated contour is used to initialize the deformation process. Thus, the algorithm works in a closed cycle in which the color is used to segment the object pixels to get the object contour and the contour is used to get the typical-color of the object. With our fast algorithm, a real-time system has been implemented on a standard PC. Results from standard test sequences and our real time system are presented.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

N. Al-Ashwal and A. Al-Junaid, "Object Tracking Using a New Level Set Model," Journal of Signal and Information Processing, Vol. 5 No. 1, 2014, pp. 17-22. doi: 10.4236/jsip.2014.51004.

References

[1] H. Meyerhoff, F. Papenmeier and M. Huff, “ObjectBased Integration of Motion Information during Attentive Tracking,” Perception, Vol. 42, No. 1, 2013, pp. 119-121.
http://dx.doi.org/10.1068/p7273
[2] A. Feldman, M. Hybinette, T. Balch and R. Cavallaro, “The Multi-ICP Tracker: An Online Algorithm for Tracking Multiple Interacting Targets,” Journal of Field Robotics, Vol. 29, No. 2, 2012, pp. 258-276.
http://dx.doi.org/10.1002/rob.21402
[3] S. B. Gokturk, C. Tomasi, B. Girod and J.-Y. Bouguet, “Model-Based Face Tracking for View-Independent Facial Expression Recognition,” 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, 21-21 May 2002, pp. 287-293.
[4] M. Wang, Y. Iwai and M. Yachida, “Expression Recognition from Time-Sequential Facial Images by Use of Expression Change Model,” 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, 14-16 April 1998, pp. 324-329.
http://dx.doi.org/10.1109/AFGR.1998.670969
[5] O. Javed and M. Shah, “Tracking and Object Classification for Automated Surveillance,” The 7th European Conference on Computer Vision (ECCV 2002), Copenhagen, 2002, pp. 343-357.
[6] R. Howarth and H. Buxton, “Visual Surveillance Monitoring and Watching,” Proceedings of European Conference on Computer Vision, Vol. 2, 1996, pp. 321-334.
[7] T. Frank, M. Haag, H. Kollnig and H.-H. Nagel, “Tracking of Occluded Vehicles in Traffic Scenes,” Proceedings of European Conference on Computer Vision, Vol. 2, 1996, pp. 485-494.
[8] N. Ray and S. T. Acton, “Active Contours for Cell Tracking,” 5th IEEE Southwest Symposium on Image Analysis and Interpretation, Sante Fe, 7-9 April 2002, pp. 274-278.
[9] E. Bardinet, L. Cohen and N. Ayache, “Tracking Medical 3D Data with a Deformable Parametric Model,” Proceedings of European Conference on Computer Vision, Vol. 1, 1996, pp. 317-328.
[10] G. Unal, H. Krim and A. Yezzi, “Active Polygon for Object Tracking,” 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT’ 02), 2002, p. 696.
[11] Y. Q. Chen, T. Huang and Y. Rui, “Mode-Based MultiHypothesis Head Tracking Using Parametric Contours,” 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, 20-21 May 2002, pp. 112-117.
[12] Y. Zhong, A. K. Jain and M.-P. Dubuisson-Jolly, “Object Tracking Using Deformable Templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 5, 2000, pp. 544-549.
http://dx.doi.org/10.1109/34.857008
[13] S. Osher and N. Paragios, “Geometric Level Set Methods in Imaging, Vision and Graphics,” Springer Verlag, 2003.
[14] J. Sethian, “Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanisms, Computer Vision, and Material Science,” Cambridge University Press, 1999.
[15] M. Kass, A. Witkin and D. Terzopoulos, “Snakes: Active Contour Models,” International Journal of Computer Vision, Vol. 1, No. 4, 1988, pp. 321-331.
http://dx.doi.org/10.1007/BF00133570
[16] S. Besson, M. Barlaud and G. Aubert, “Detection and Tracking of Moving Objects Using a New Level Set Based Method,” Proceedings of ICPR, Vol. 3, 2000, pp. 1100-1105.
[17] D. Adalsteinsson and J. Sethian, “A Fast Level Set Method for Propagating Interfaces,” Journal of Computational Physics, Vol. 118, 1995, pp. 269-277.
[18] N. Paragios and R. Deriche, “Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, 2000, pp. 266-280.
[19] Y. G. Shi and W. C. Karl, “Real-Time Tracking Using Level Sets,” Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR ‘05), Vol. 2, 2005, pp. 34-41.
[20] H. Shariat and K. E. Price, “Motion Estimation with more than Two Frames,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 12, No. 5, 1990, pp. 417-432.
http://dx.doi.org/10.1109/34.55102
[21] B. K. P. Horn and B. Schunck, “Determining Optical Flow,” Artificial Intelligence, Vol. 17, No. 1-3, 1981, pp. 185-203. http://dx.doi.org/10.1016/0004-3702(81)90024-2
[22] X. Zhuang, T. S. Huang, N. Ahuja and R. M. Haralick, “A Simplified Linear Optic Flow-Motion Algorithm,” Computer Vision, Graphics and Image Processing, Vol. 42, No. 3, 1988, pp. 334-344.
http://dx.doi.org/10.1016/S0734-189X(88)80043-4
[23] L. S. Davis, Z. Wu and H. Sun, “Contour-Based Motion Estimation,” Computer Vision, Graphics and Image Processing, 1982, pp. 313-326.
[24] R. Bergevin and M. D. Levine, “Extraction of Line Drawing Features for Object Recognition,” Pattern Recognition, Vol. 25, No. 3, 1992, pp. 319-334.
http://dx.doi.org/10.1016/0031-3203(92)90113-W
[25] C. Cédras and M. Shah, “Motion-Based Recognition: A Survey,” Image and Vision Computing, Vol. 13, No. 2, 1995, pp. 129-155.

  
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