Field programmable Gate Array based Real Time Object Tracking using Partial Least Square Analysis
In this paper, we proposed an object tracking algorithm in real time implementation of moving object tracking system using Field programmable gate array (FPGA). Object tracking is considered as a binary classification problem and one of the approaches to this problem is that to extract appropriate features from the appearance of the object based on partial least square (PLS) analysis method, which is a low dimension reduction technique in the subspace. In this method, the adaptive appearance model integrated with PLS analysis is used for continuous update of the appearance change of the target over time. For robust and efficient tracking, particle filtering is used in between every two consecutive frames of the video. This has implemented using Cadence and Virtuoso software integrated environment with MATLAB. The experimental results are performed on challenging video sequences to show the performance of the proposed tracking algorithm using FPGA in real time.
D. A. Ross, J. Lim, R. S. Lin, Yang, M. H. Incremental learning for robust visual tracking, International journal of computer vision, 77 (2008) 125-141.
Q. Li, Y. Yan, H. Wang, Discriminative weighted sparse partial least squares for human detection, IEEE Transactions on Intelligent Transportation Systems, 17 (2015) 1062-1071.
H. Medeiros, J. Park, A. Kak, Distributed object tracking using a cluster-based kalman filter in wireless camera networks, IEEE Journal of Selected Topics in Signal Processing, 2 (2008) 448-463.
M. Amiri, H. R. Rabiee, F. Behazin, M. Khansari, (2003) A new wavelet domain block matching algorithm for real-time object tracking, In Proceedings 2003 International Conference on Image Processing, IEEE, 3.
P. Y. Yeoh, S. A. R. Abu-Bakar, (2003) Accurate real-time object tracking with linear prediction method, In Proceedings 2003 International Conference on Image Processing, IEEE, 3.
Durand, J., & Hutchinson, S. (2003). Real-time object tracking using multi-res. critical points filters, In 2003 IEEE International Conference on Robotics and Automation, IEEE, 2, 1682-1687
P. Lanvin, J. C. Noyer, M. Benjelloun, M. Yeary, Y. Zhai, (2005) Hybrid particle filtering for real time object tracking, In Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, IEEE, 761-764.
J. U. Cho, S. H. Jin, X. Dai Pham, J. W. Jeon, J. E. Byun, H. Kang, (2006) A real-time object tracking system using a particle filter, In 2006 IEEE/RSJ international conference on intelligent robots and systems, IEEE, 2822-2827.
J. Wang, Y. Yagi, Integrating color and shape-texture features for adaptive real-time object tracking, IEEE Transactions on Image Processing, 17 (2008) 235-240.
W. Yaonan, W. Qin, Y. Hongshan, Effective method for tracking multiple objects in real-time visual surveillance systems, Journal of Systems Engineering and Electronics, 20 (2009) 1167-1178.
J. Yin, Y. Han, J. Li, A. Cao, (2009) Research on real-time object tracking by improved Cam Shift, In 2009 International Symposium on Computer Network and Multimedia Technology, IEEE, 1-4.
A. Abdel-Hadi, (2010) Real-time object tracking using color-based Kalman particle filter. In The 2010 International Conference on Computer Engineering & Systems, IEEE, 337-341.
D. Chakraborty, D. Patra, (2010) Real time object tracking based on segmentation and distance minimization, In 2010 Annual IEEE India Conference, IEEE, 1-4.
E. Emami, M. Fathy, (2011) Object tracking using improved camshift algorithm combined with motion segmentation, In 2011 7th Iranian Conference on Machine Vision and Image Processing, IEEE, 1-4.
S. Nasrullah, D. A. Khan, (2011) A novel algorithm for real time moving object tracking, In 2011 International Conference on Image Information Processing, IEEE, 1-5.
Copyright (c) 2020 Somasundaram D., Kumaresan N, Vanitha S
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