Object Tracking System Using Mean Shift Algorithm and Implementation on FPGA

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-18 Number-6
Year of Publication : 2014
Authors : Hitesh Patel , Ashish Singhadia
DOI :  10.14445/22315381/IJETT-V18P260


Hitesh Patel , Ashish Singhadia "Object Tracking System Using Mean Shift Algorithm and Implementation on FPGA ", International Journal of Engineering Trends and Technology (IJETT), V18(6),293-296 Dec 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


Various attempts have been made to implement human vision in real-time computer vision. In most cases, however, this capability has been found to be too complex to implement on a practical systems. In short, human vision capability looks like a simple system but in reality it is difficult to model. One of the difficulties is the computational complexity required to process large amounts of data in real-time, and consequently, the design of real-time object tracking using vision remains a challenging problem. However, in this thesis, we propose and implement hardware architecture for real-time object tracking system. We have chosen Mean-Shift algorithm for implementation. We will implement firstly this algorithm on MATLAB and simulated in different environments to verify the performance. After approving its robustness we implemented the algorithm on hardware.


[1] Su Liu, Alexandros Papakonstantinou, Hongjun Wang1, Deming Chen 978-0-7695-4448-9/112011IEEE
[2] J. Aloimonos and A. Badyopadhyay. “Active Vision.” In IEEE intl Conf. on Computer Vision, pp. 35-54, 1987.
[3] Comaniciu D, Comaniciu, Ramesh V, and Meer P, “Real-Time Tracking of Non-Rigid Objects using Mean Shift,” In Proc. Of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 142-149, 2000.
[4] VEENMAN, C., REINDERS, M., AND BACKER, E. 2001. Resolving motion correspondence for densely moving points. IEEE Trans. Patt. Analy. Mach. Intell. 23, 1, 54–72.
[5] Ali U, Malik M and Munawar K, “FPGA/SOFT-processor based real time tracking system,” In Fifth Southern Conference on Programmable logic, pp 33,2009.
[6] COMANICIU, D., RAMESH, V., ANDMEER, P. 2003. Kernel-based object tracking. IEEE Trans. Patt. Analy. Mach. Intell. 25, 564–575.
[7] YILMAZ, A., LI, X., AND SHAH, M. 2004. Contour based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Patt. Analy. Mach. Intell. 26, 11, 1531–1536.
[8] BALLARD, D. AND BROWN, C. 1982. Computer Vision. Prentice-Hall.
[9] ZHU, S. AND YUILLE, A. 1996. Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Patt. Analy. Mach. Intell. 18, 9, 884–900.
[10] ELGAMMAL, A.,DURAISWAMI, R.,HARWOOD, D., ANDDAVIS, L. 2002. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE 90, 7, 1151–1163.
[11] Fukunaga K, and Hostetler LD, “The estimation of the gradient of a density function, with application in pattern recognition,” IEEE Trans. Information Theory, vol. 21, pp.32-40,1975.
[12] Zhi-Qiang Wen and Zi-Xing Cai, “Mean Shift Algorithm and its Application in Tracking of Objects,” In Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August, 2006

object tracking; Mean-Shift Algorithm ;FPGA