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Reseach Article

Automatic Detection of Events and Tracking of Moving Objects in Video Sequences

by Mohammad Mahmood Otoom, Khalid Nazim Abdul Sattar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 1
Year of Publication: 2015
Authors: Mohammad Mahmood Otoom, Khalid Nazim Abdul Sattar
10.5120/21193-3851

Mohammad Mahmood Otoom, Khalid Nazim Abdul Sattar . Automatic Detection of Events and Tracking of Moving Objects in Video Sequences. International Journal of Computer Applications. 120, 1 ( June 2015), 29-35. DOI=10.5120/21193-3851

@article{ 10.5120/21193-3851,
author = { Mohammad Mahmood Otoom, Khalid Nazim Abdul Sattar },
title = { Automatic Detection of Events and Tracking of Moving Objects in Video Sequences },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 1 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number1/21193-3851/ },
doi = { 10.5120/21193-3851 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:07.275885+05:30
%A Mohammad Mahmood Otoom
%A Khalid Nazim Abdul Sattar
%T Automatic Detection of Events and Tracking of Moving Objects in Video Sequences
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 1
%P 29-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital video is being used widely in a variety of applications such as surveillance and security. Big amount of video in surveillance and security requires systems capable to process video automatically to detect events and track moving objects to alleviate the load on humans and enable preventive actions when events are detected [4]. our paper focuses to develop an intelligent visual surveillance system to replace the traditional passive video surveillance that is proving ineffective as the number of cameras exceeds the capability of human operators to monitor them, and it is able to track objects within a maximum solid angle speed which is measured at about 0. 3 to 0. 2 radian per second, further it also depends on the complexity of the system and the processor speed as well.

References
  1. Gonzalez, R. C. & Woods, R. E. 2007. Digital Image Processing. third edition. Prentice Hall.
  2. Bovik, A. 2005. Handbook of Image and Video Processing. Second edition. Canada, Academic Press.
  3. Singha, K. Kapoorb, R. 2014. Image enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization. Optik - International Journal for Light and Electron Optics, September.
  4. Singh, B. Singh, D. Singh, G. Sharma, N. 2014. Motion detection for video surveillance. Signal Propagation and Computer Technology (ICSPCT) International Conference, 12-13 July.
  5. Khan, M. , Khan, E. Abbasi, Z. A. 2014. Segment selective dynamic histogram equalization for brightness preserving contrast enhancement of images. Optik - International Journal for Light and Electron Optics, February.
  6. Singha, K. Kapoorb, R. 2014. Image enhancement using Exposure based Sub Image Histogram Equalization. Pattern Recognition Letters, 15 January.
  7. Al-Berry, N. Salem, A. Hussein, S. Tolba, F. 2013. Motion Detection using Wavelet-enhanced Accumulative Frame Differencing . Computer Engineering & Systems (ICCES) International Conference, November.
  8. Catherine. 2013. Implementation of background subtraction algorithm for motion detection. New Media Studies (CoNMedia) Conference, November.
  9. Kamaraj, M. Balakrishnan. 2013. An improved motion detection and tracking of active blob for video surveillance. Computing, Communications and Networking Technologies (ICCCNT) Fourth International Conference, July.
  10. Wirayuda, B. Laksitowening, A. Sthevanie, F. Rismala, R. 2013. Development methods for hybrid motion detection (frame difference-automatic threshold). Information and Communication Technology (ICoICT) International Conference, March.
  11. Celik, T. 2012. Two-dimensional histogram equalization and contrast enhancement. Pattern Recognition, October.
  12. Huang, S. Cheng, F. 2012. Motion detection with pyramid structure of background model for intelligent surveillance systems. Engineering Applications of Artificial Intelligence, October.
  13. Bang, J. Kim, D. Eom, H. 2012. Motion Object and Regional Detection Method Using Block-Based Background Difference Video Frames, Embedded and Real-Time Computing Systems and Applications (RTCSA) IEEE 18th International Conference, August.
  14. Cheng, F. Ruan, S. 2012. Accurate Motion Detection Using a Self-Adaptive Background Matching Framework. Intelligent Transportation Systems IEEE Transactions, June.
  15. Huang, S. 2011. An Advanced Motion Detection Algorithm With Video Quality Analysis for Video Surveillance Systems. Circuits and Systems for Video Technology IEEE Transactions, January.
  16. Zhao, Y. Liu, J. 2010. An improved method for human motion detection and application. Image and Signal Processing (CISP) 3rd International Congress, October.
  17. Fang, L. & Meng, Z. 2009. Smart Motion Detection Surveillance System. IEEE International Conference on Education Technology and Computer, 17-20 April.
  18. Lu, N. , Wang, J. , Wu, Q. H. & Yang, L. 2008. An Improved Motion Detection Method for Real-Time Surveillance. IAENG International Journal of Computer Science, 19 February.
  19. Yilmaz, A. Javed, O. & Shah, M. 2006. Object Tracking: A Survey. ACM computing surveys, December.
  20. Piccardi, M. 2004. Background subtraction techniques: a review. IEEE International Conference on Systems, Man and Cybernetics, 10-13 Oct.
  21. Hu, W. Tan, T. Wang, L. & Maybank, S. 2004. Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on systems, man and cybernetics, August.
  22. Cheung, S. S. & Kamath, C. 2004. Robust techniques for background subtraction in urban traffic video. SPIE Electronic Imaging In Proceedings of Video Communications and Image Processing, January.
  23. Stauffer, C. & Grimson, W. E. L. 1999. Adaptive background mixture models for real-time tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 23-25 June.
  24. Friedman, N. & Russell, S. 1997. Image segmentation in video sequences: a probabilistic approach. Computer Science Division, University of California, Berkeley, Thirteenth Conference on Uncertainty in Artificial Intelligence.
Index Terms

Computer Science
Information Sciences

Keywords

Surveillance background subtraction cumulative distribution function (CDF) probability density function (PDF) Mixture of Gaussians (MoG).