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International Journal of Computer Applications (0975 8887)
Volume 64 No.13, February 2013
34
Figure 9: The Output video sequence for Motion detection
Figure 10: The Output video sequence for Intrusion
detection.
5. CONCLUSION
An object tracking algorithm for video, based on image
segmentation and pattern matching of the segmented objects
between frames in a simple feature space is proposed.
Simulation and real time results for frame sequences with
video sequences verify the suitability of the algorithm for
reliable moving object tracking. The proposed method uses
single camera method it can be further extended to multiple
camera method to enhance the security in surveillance system.
The proposed algorithm can be optimized further to reduce
the processing time and can be implemented using other
processors to verify its processing speed.
6. REFERENCES
[1] M. Valera and S.A. Velastin, “Intelligent distributed
surveillance systems: a review.” IEEE Proc. Vision,
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[2] N. Sulman, T. Sanocki, D. Goldgof, and R. Kasturi,
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[3] W. Hu, T. Tan, L. Wang, and S. Maybank “A survey on
visual surveillance of object motion and behaviors,”
IEEE Trans. Systems, Man, and Cybernetics Part C, vol.
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[4] Massimo Piccardi “Background subtraction techniques:
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Centre of Excellence for Autonomous Systems (CAS)
Faculty of Engineering, UTS, April 15,2004
[5] J. Heikkila and O. Silven, “A real-time system for
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on Visual Surveillance, pp. 74-81, 1999.
[6] C. Stauffer and W. E. L. Grimson, “Adaptive background
mixture models for real-time tracking,” IEEE Int.
Conference on Computer Vision and Pattern
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[7] G. Halevy and D.Weinshall, “Motion of disturbances:
detection and tracking of multibody non-rigid motion,”
IEEE Int. Conference on Computer Vision and Patter
Recognition, pp. 897-902, 1997.
[8] R. Cutler and L. Davis, “View-based detection and
analysis of periodic motion,” Int. Conference on Pattern
Recognition, pp. 495-500, 1998.
[9] R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam,
“Image change detection algorithms: a systematic
survey,” IEEE Trans. on Image Processing, vol. 14, no.
3, pp. 294-307, 2005.
[10] A. Dominguez-Caneda, C. Urdiales, and F. Sandoval,
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using virtual reality based prediction,” Electrotechnical
Conference (MELECON), pp. 466-469, 2006.
[11] S-C. Cheung and C. Kamath, “Robust background
subtraction with foreground validation for Urban Traffic
Video,” EURASIP Journal on Applied Signal
Processing, vol. 14, pp. 1-11, 2005.
[12] Rafael C Gonzalez, Richard E Woods, “Digital Image
Processing”.
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