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Object Detection and Tracking using Background Subtraction and Connected Component Labeling

by Asad Abdul Malik, Amaad Khalil, Hameed Ullah Khan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 13
Year of Publication: 2013
Authors: Asad Abdul Malik, Amaad Khalil, Hameed Ullah Khan
10.5120/13168-0421

Asad Abdul Malik, Amaad Khalil, Hameed Ullah Khan . Object Detection and Tracking using Background Subtraction and Connected Component Labeling. International Journal of Computer Applications. 75, 13 ( August 2013), 1-5. DOI=10.5120/13168-0421

@article{ 10.5120/13168-0421,
author = { Asad Abdul Malik, Amaad Khalil, Hameed Ullah Khan },
title = { Object Detection and Tracking using Background Subtraction and Connected Component Labeling },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 13 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number13/13168-0421/ },
doi = { 10.5120/13168-0421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:08.955955+05:30
%A Asad Abdul Malik
%A Amaad Khalil
%A Hameed Ullah Khan
%T Object Detection and Tracking using Background Subtraction and Connected Component Labeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 13
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness. The subtracted image is then tracked using connected component labeling. The proposed system eliminates the shadow and provides 79% accuracy.

References
  1. DeepaKumari, ShamikTiwari, Deepika Gupta, Raina , "Analysis on Adaptive Moving Objects via Robot Vision Implementations by Detection Techniques", International Journal of Scientific & Engineering Research, Volume 3, Issue 4, April-2012.
  2. ChandrashekharD. Badgujar, DipaliP. Sapkal, "A Survey on Object Detect, Track and Identify Using Video Surveillance", IOSR Journal of Engineering (IOSRJEN), Volume 2, Issue 10 (October 2012), PP 71-76.
  3. W. L. Khong, W. Y. Kow, H. T. Tan, H. P. Yoong, K. T. K. Teo, "Kalman Filtering Based Object Tracking in Surveillance Video System", Proceedings of the 3rd (2011) CUTSE International Conference, Miri, Sarawak, Malaysia, 8-9 Nov, 2011.
  4. Chirag I. Patel and Ripal Patel, "Illumination Invariant Moving Object Detection", International Journal of Computer and Electrical Engineering, Vol. 5, No. 1, February 2013.
  5. Shireen Y. Elhabian, Khaled M. El-Sayed and Sumaya H. Ahmed, "Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art", Recent Patents on Computer Science 2008, 1, 32-54.
  6. U. Chandrasekhar, Tapankumar Das, "A Survey of Techniques for Background Subtraction and Traffic Analysis on Surveillance Video", ISSN, Tapankumar das et al, UNIASCIT, Vol 1 (3), 2011, 107-113.
  7. Bo yang, Chang Huang, Ram Nevatia, "Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs", Computer Vision and Image Understanding, Volume 115 Issue 11, November, 2011, Pages 1473-1482.
  8. Ritika, Gianetan Singh Sekhon, "Moving Object Analysis Techniques In Videos - A Review",IOSR Journal of Computer Engineering (IOSRJCE) ISSN, 2278-0661 Volume 1, Issue 2 (May-June 2012), PP 07-12
  9. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey",ACM Computing Surveys, Vol. 38, No. 4, 1-45, 2006
  10. Lucchese L. , Mitra S. K. Color image segmentation: A state-of-the-art survey. in Proc. Indian National Science Academy(INSA-A), vol. 67, A, New Delhi, India, Mar. 2001, pp. 207–221.
  11. Stauffer C, Grimson W. E. L. Adaptive background mixture models for real-time tracking. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Ft. Collins, 1999: 246-252
  12. Power P. W. , Schoonees J. A. Understanding background mixture models for foreground segmentation. In Proceedings Image and Vision Computing, 2002, pp:267-271.
  13. Lee D. S. , Hull J. , Erol B. A Bayesian framework for gaussian mixture background modeling. in Proceedings of IEEE International Confererence on Image Processing, 2003, pages:973-976
  14. S. Gundimada, Li Tao, and v. Asari, "Face detection technique based on intensity and skin color distribution," in 2004 International Conference on Image Processing, Otc. 2004, vol. 2, pp. 1413–1416.
  15. K. P. Seng, A. Suwandy, and L. -M. Ang, "Improved automatic face detection technique in color images," in IEEE Region 10 Conference TENCON 2004, Nov. 2004, vol. 1,pp. 459–462.
Index Terms

Computer Science
Information Sciences

Keywords

Object tracking detection Background Subtraction color distortion

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