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Implementation of Motion detection and Tracking based on Mathematical Morpohology

by Sheetal Balsaraf, Uday Joshi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 15
Year of Publication: 2013
Authors: Sheetal Balsaraf, Uday Joshi
10.5120/13938-1904

Sheetal Balsaraf, Uday Joshi . Implementation of Motion detection and Tracking based on Mathematical Morpohology. International Journal of Computer Applications. 80, 15 ( October 2013), 22-28. DOI=10.5120/13938-1904

@article{ 10.5120/13938-1904,
author = { Sheetal Balsaraf, Uday Joshi },
title = { Implementation of Motion detection and Tracking based on Mathematical Morpohology },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 15 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number15/13938-1904/ },
doi = { 10.5120/13938-1904 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:38.115466+05:30
%A Sheetal Balsaraf
%A Uday Joshi
%T Implementation of Motion detection and Tracking based on Mathematical Morpohology
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 15
%P 22-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Security is becoming the primary concern of society. Having a security system is therefore becoming a requirement. Video surveillance plays a vital role in security systems. Consider a video stream taken by a fixed camera to monitor motion-restricted area. The system works on a real-time video,from the camera, which is accessed frame to frame. The proposed system uses mathematical morphology for implementation. The system is implemented using a generic tool for Computer Vision applications, EmguCV package in OpenCV. The system uses absolute differencing for motion detection. Each video frame goes through image filtering, for noise removal. For handling illumination changes edge feature, contour extraction is used, to avoid spurious movements appropriate threshold is selected for respective area to be surveillance. Once motion due to target object is detected, alert is generated continuously until the alert is not de-activated. After the motion is detected, the object causing motion is tracked indefinitely in live video. Even if the subject leaves the area, it's last location is bounded and seen on the system. It's movements are tracked and respective frames are stored on hard disk drive.

References
  1. Masayuki Yokoyama, Tomaso Poggio, A Contour-Based Moving Object Detection and Tracking, Proceedings of: Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop.
  2. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
  3. Stauffer, C. ; Grimson, W. E. L. , "Learning patterns of activity using real-time tracking", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug 2000.
  4. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction," in Proc. European Conf. Computer Vision,vol. II, May 2000, pp. 751–767.
  5. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, Performance of optical ?ow techniques," Int'l J. Computer Vision, vol. 12(1), pp. 43–77,February 1994.
  6. J. H. Duncan and T. -C. Chou, "On the detection of motion and the computation of optical ?ow," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 3, pp. 346–352, March 1992.
  7. V. Caselles and B. Coll, "Snakes in movement," SIAM J. Numerical Analysis, vol. 33, pp. 2445–2456, 1996.
  8. J. Sethian, "Level set methods and fast marching methods," Cambridge Univ. Press, 1999.
  9. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," Int'l J. Computer Vision, pp. 321–331, 1988.
  10. D. Cohen, "On active contour models and balloons," CVGIP: Image Understanding, vol. 53, pp. 211–218, 1991.
  11. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," in Proc. IEEE Int'l Conf. Computer Vision, 1995, pp. 694–699.
  12. N. Paragios and R. Deriche, "Geodesic active contours and level sets for the detection and tracking of moving objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 3, pp. 266–280, March 2000.
  13. Willow Garage, Video Surveillance [Online]. Available: http://opencv. willowgarage. com/wiki/VideoSurveillance.
  14. Mathematical Morphology, Thresholding, Gaussian Filter, Blob Detection [Online]. Available: http://en. wikipedia. org
  15. OpenCV 2. 4. 2 Documentation [online]. Available: http://opencv. org/documentation. html
  16. Gary Bradski and Adrian Kaehler, "Learning OpenCV " O'Reilly Media, Inc. , 1st Edition, Sep '08.
Index Terms

Computer Science
Information Sciences

Keywords

Motion detection and tracking video surveillance

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