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Reseach Article

A Survey on Real Time Object Tracking

Published on December 2015 by Kaustubh Bhikan Ahirrao, Nitin N. Patil
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 7
December 2015
Authors: Kaustubh Bhikan Ahirrao, Nitin N. Patil
1808edaa-8125-4dbc-80fa-1f00aac90ac4

Kaustubh Bhikan Ahirrao, Nitin N. Patil . A Survey on Real Time Object Tracking. National Conference on Advances in Computing. NCAC2015, 7 (December 2015), 43-46.

@article{
author = { Kaustubh Bhikan Ahirrao, Nitin N. Patil },
title = { A Survey on Real Time Object Tracking },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 7 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 43-46 },
numpages = 4,
url = { /proceedings/ncac2015/number7/23409-5081/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Kaustubh Bhikan Ahirrao
%A Nitin N. Patil
%T A Survey on Real Time Object Tracking
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 7
%P 43-46
%D 2015
%I International Journal of Computer Applications
Abstract

This paper discussers a survey of various techniques in the field of object tracking and tracking in video for improving the security. Our goal is to review various techniques of detection of the moving object and after detection, tracking of moving object. Detection of the moving object is difficult task and most difficult task is to track the detected object. Detect the moving object is important task track that moving object is the most challenging part because require detail information of object like shape of object, location of object. In this survey I review various techniques like temporal frame differencing, background subtraction. Object tracking algorithm for moving object is a quite difficult. For tracking first detection is important low level task. In future aim to enhance the exiting method to improve the performance.

References
  1. W. Zhong, H. Lu, and M. -H. Yang, "Robust object tracking via sparsitybased collaborative model," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2012, pp. 1838–1845.
  2. X. Jia, H. Lu, and M. -H. Yang, "Visual tracking via adaptive structural local sparse appearance model," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2012, pp. 1822–1829.
  3. Kinjal A Joshi, Darshak G. Thakore, "A Survey on Moving Object Detection and Tracking in Video Surveillance System," in International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-2, Issue-3, July 2012.
  4. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Comput. Surveys, vol. 38, no. 4, pp. 1–45, 2006.
  5. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Robust visual tracking via multi-task sparse learning," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2012, pp. 2042–2049.
  6. J. Santner, C. Leistner, A. Saffari, T. Pock, and H. Bischof, "PROST:Parallel robust online simple tracking," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2010, pp. 723–730.
  7. X. Mei and H. Ling, "Robust visual tracking using L1 minimization," in Proc. IEEE 12th Int. Conf. Comput. Vis. , Oct. 2009, pp. 1436–1443.
  8. B. Liu, J. Huang, L. Yang, and C. Kulikowsk, "Robust tracking using local sparse appearance model and k-selection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2011, pp. 1313–1320.
  9. R. Liu, J. Cheng, and H. Lu," A robust boosting tracker with minimum error bound in a co-training framework. In ICCV, 2009. "
  10. F. Tang, S. Brennan, Q. Zhao, and H. Tao, co tracking using semi supervised support vector machine. In ICCV,2007.
Index Terms

Computer Science
Information Sciences

Keywords

Object Detection Background Subtraction Frame Differencing Object Tracking Video Surveillance.