Fpga Based Real-Time Target Tracking

FPGA based Real-time Target Tracking
By
Muhammad Israr Azeem
(BEES/F08/0173)
cell+92 03078484033 Muhammad Tahir (BEES/F08/0134) Maryam Firdous (BEES/F08/0119) Under the supervision of
Dr. Fahad Azim
2012
Faculty of Engineering Sciences and Technology
Hamdard Institute of Information Technology
Hamdard University, Main Campus, Karachi, Pakistan

FPGA based Real-time Target Tracking
By
Muhammad Israr Azeem
(BEES/F08/0173)
Muhammad Tahir
(BEES/F08/0134)
Maryam Firdous
(BEES/F08/0119)

A Project Presented to the
Faculty of Engineering Sciences and Technology
Hamdard Institute of Information Technology
In partial fulfillment of the requirements
For the degree
Bachelors of Engineering
In
Electronics
Faculty of Engineering Sciences and Technology
Hamdard Institute of Information Technology
Hamdard University, Main Campus, Karachi, Pakistan

Faculty of Engineering Sciences and Technology
Hamdard Institute of Information Technology
Hamdard University, Main Campus, Karachi, Pakistan
CERTIFICATE
This project “FPGA based Real-time Target Tracking” presented by Muhammad Israr Azeem , Muhammad Tahir and Maryam Firdous under the direction of their project advisor’s and approved by the project examination committee, has been presented to and accepted by the Hamdard Institute of Information Technology, in partial fulfillment of the requirements for the bachelor degree of Electronics Engineering. ____________________________Dr. Fahad Azim(Project Advisor)
____________________________ (Project Co advisor)____________________________Prof. Dr. Vali uddin(Director, HIIT) | __________________________(Member) ____________________________(Member) ___________________________(Date) |

References: [4] H. Kimura and T. Shibata, “Simple-architecture motiondetectionanalog V-chip based oquasi-two-dimensional processing,”Extended Abstracts of the 2002 International Conferenceon Solid State Devices and Materials, pp. 240-241, 2002. [5] T. Morimoto et al., “Object tracking in video pictures based onimage segmentation and pattern matching,” Proc. of the IEEEInt. Symp. on Circ. and Syst.(ISCAS2005), pp.3215-3218, 2005. [8] H. Adachi et al., “Image-scan architecture for efficientFPGA/ASIC implementation of video-segmentation by regiongrowing,” Proc. of The International SoC Design Conference( ISOCC2005), pp [9] Meyer, D.; Denzler, J. & Niemann, H. (1998). Model based extraction of articulated objects inimage sequences for gait analysis, Proceeding of IEEE Int. Conf. Image Proccessing, pp. [10] Phung, S.; Chai, D. & Bouzerdoum, A. (2003). Adaptive skin segmentation in color images, Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 3, pp. 353-356.Cho, K. M.; Jang, J. H. & Hong, K. S. (2001). Adaptive skin-color filter, Pattern Recognition,pp. 1067-1073. [11] Broida, T. & Chellappa, R. (1986). Estimation of object motion parameters from noisy images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol [12] Ristic, B.; Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman filter: Particle filters fortracking applications, Artech House. [8] Isard, M. & Blake, A. (1998). CONDENSATION –Conditional Density Propagation for Visual Tracking, Intl models, Journal of Comput. and Graph. Stat., Vol. 5, No. 1, pp. 1–25.Gordon, N.; Salmon, D. & Smith, A. (1993). Novel approach to nonlinear/non-Gaussian [14] Bayesian state estimation, IEE Proc pp. 107-113.Tanizaki, H. (1987). Non-Gaussian state-space modeling of non-stationary time series, [15] Journal of American Statistic Association, Vol [16] Nummiaro, K.; Koller, E. & Gool, L. (2002). An adaptive color-based particle filter, Image andVision Computing, Vol. 25, No. 1, pp. 99–110. [17] Perez, P.; Hue, C. & Vermaak, J. (2002). Color-based probabilistic tracking, Proc. of the 7th European Conference on Computer Vision, 661–675. [18] Comaniciu, D.; Ramesh, V. & Meer, P. (2000). Real-time tracking of non-rigid objects using mean shift, Proc Comaniciu, D.; Ramesh, V. & Meer, P. (2003), Kernel-based object tracking, IEEE Trans. on [19] Pattern Analysis and Machine Intelligence, Vol [20] Simon, D. (2006). Optimal State Estimation, Jhon Wiey & Sons, Inc, Publication. Freeman, W. & Adelson, E. (1991). The Design and Use of Steerable Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol