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Reseach Article

Algorithms for Tracking Moving Targets: A Review

by Marcelo Queiroz De Lima Brilhante, Pedro Felipe Teixeira Sousa, Auzuir Ripardo De Alexandria
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 2
Year of Publication: 2017
Authors: Marcelo Queiroz De Lima Brilhante, Pedro Felipe Teixeira Sousa, Auzuir Ripardo De Alexandria
10.5120/ijca2017915929

Marcelo Queiroz De Lima Brilhante, Pedro Felipe Teixeira Sousa, Auzuir Ripardo De Alexandria . Algorithms for Tracking Moving Targets: A Review. International Journal of Computer Applications. 180, 2 ( Dec 2017), 1-8. DOI=10.5120/ijca2017915929

@article{ 10.5120/ijca2017915929,
author = { Marcelo Queiroz De Lima Brilhante, Pedro Felipe Teixeira Sousa, Auzuir Ripardo De Alexandria },
title = { Algorithms for Tracking Moving Targets: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 2 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number2/28769-2017915929/ },
doi = { 10.5120/ijca2017915929 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:29.221308+05:30
%A Marcelo Queiroz De Lima Brilhante
%A Pedro Felipe Teixeira Sousa
%A Auzuir Ripardo De Alexandria
%T Algorithms for Tracking Moving Targets: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 2
%P 1-8
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Currently there is a huge variety of methods that have been developed to determine the location of a moving object using digital image. Multiple approaches have been proposed in the field of computational vision such as: obtaining parameters of position and velocity of an object over time; following suspicious people in a given environment; automating the collection of information of vehicle plates; moving a camera to automatically follow a ball in a soccer match; or deciding whether a product on a production line in the industry is within the quality standards. This article compiles a brief summary of four techniques related to tracking objects in digital videos. The objective of this work is to present some of the main methods developed so far, show the general structure of the algorithm, address the mathematical fundamentals and their characteristics, and list the important papers and applications that use them. The review is based on some of the work and theory of methods performed so far. The progress made so far and the main challenges still unresolved will also be evaluated. Among the several studies, was observed that the various techniques can be used in various combinations to solve a given problem in computer vision, thus mastering such topics is essential for the development of technology in computer vision systems.

References
  1. Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. Acm computing surveys (CSUR), 38(4):13, 2006.
  2. Jake K Aggarwal and Quin Cai. Human motion analysis: A review. In Nonrigid and Articulated Motion Workshop, 1997. Proceedings., IEEE, pages 90–102. IEEE, 1997.
  3. Ahmed Elgammal, Ramani Duraiswami, David Harwood, and Larry S Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 90(7):1151–1163, 2002.
  4. Michal Irani and Prabu Anandan. Video indexing based on mosaic representations. Proceedings of the IEEE, 86(5):905– 921, 1998.
  5. Manuel Jes´us Mar´in-Jim´enez, Andrew Zisserman, Marcin Eichner, and Vittorio Ferrari. Detecting people looking at each other in videos. International Journal of Computer Vision, 106(3):282–296, 2014.
  6. Pooja Kudav and Pranav Acharya. Automated traffic control system using big data and cognitive analysis. International Journal of Computer Applications, 151(10), 2016.
  7. Victor Vaquero, Ely Repiso, Alberto Sanfeliu, John Vissers, and Maurice Kwakkernaat. Low cost, robust and real time system for detecting and tracking moving objects to automate cargo handling in port terminals. In Robot 2015: Second Iberian Robotics Conference, pages 491–502. Springer, 2016.
  8. Mansi Manocha and Parminder Kaur. Roi based video object tracking using mean kernel profile of histogram. International Journal of Computer Applications, 3(8), 2014.
  9. Geraldo Silveira, Jos RH CARVALHO, Samuel Siqueira BUENO, and Marconi K MADRID. Uma revisao das tecnicas de controle servo visual de robos. 2010.
  10. Davi Yoshinobu Kikuchi. Sistema de controle servo visual de uma camera pan-tilt com rastreamento de uma regiao de referencia. PhD thesis, Universidade de Sao Paulo, 2007.
  11. Yannick Benezeth, Pierre-Marc Jodoin, Bruno Emile, H´el`ene Laurent, and Christophe Rosenberger. Review and evaluation of commonly-implemented background subtraction algorithms. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1–4. IEEE, 2008.
  12. Omar Javed and Mubarak Shah. Tracking and object classification for automated surveillance. Computer VisionECCV 2002, pages 439–443, 2006.
  13. L Shapiro and G Stockman. Computer vision, pp prenticehall. New Jersey, USA, 2001.
  14. Simon Medeiros Soares, Thiago de Castro Turino, Mariane Rembold Petraglia, and Jose Gabriel Rodriguez Carneiro Gomes. Rastreamento de objetos utilizando processamento de imagem. 2010.
  15. Maur´icio Marengoni and Stringhini Stringhini. Tutorial: Introduc¸ ˜ao `a vis˜ao computacional usando opencv. Revista de Inform´atica Te´orica e Aplicada, 16(1):125–160, 2009.
  16. Keinosuke Fukunaga and Larry Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on information theory, 21(1):32–40, 1975.
  17. Jue Wang, Bo Thiesson, Yingqing Xu, and Michael Cohen. Image and video segmentation by anisotropic kernel mean shift. Computer Vision-ECCV 2004, pages 238–249, 2004.
  18. Guilherme Machado Gagliardi et al. Sistema robusto de acompanhamento de trajetoria de alvos moveis. PhD thesis, UNIVERSIDADE DE SA˜ O PAULO, 2014.
  19. Yizong Cheng. Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence, 17(8):790–799, 1995.
  20. Dorin Comaniciu and Peter Meer. Mean shift analysis and applications. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1197–1203. IEEE, 1999.
  21. Rafael C Gonzalez, Steven L Eddins, and Richard E Woods. Digital Image Publishing Using MATLAB. Prentice Hall, 2004.
  22. Zhiyu Zhou, Dichong Wu, Xiaolong Peng, Zefei Zhu, and Kaikai Luo. Object tracking based on camshift with multifeature fusion. JSW, 9(1):147–153, 2014.
  23. Jianbo Shi et al. Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94., 1994 IEEE Computer Society Conference on, pages 593–600. IEEE, 1994.
  24. Carlo Tomasi and Takeo Kanade. Detection and tracking of point features. 1991.
  25. Bruce D Lucas, Takeo Kanade, et al. An iterative image registration technique with an application to stereo vision. 1981.
  26. Jean-Yves Bouguet. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation, 5(1-10):4, 2001.
  27. Gloria Liliana Lopez Munoz. Analise comparativa das tecnicas de controle servo-visual de manipuladores roboticos baseadas em posicao e em imagem. 2011.
  28. Etienne Vincent and Robert Laganiere. An empirical study of some feature matching strategies. In Proc. Conf. Vision Interface, Calgary, Canada, pages 139–145, 2002.
  29. Henrik Andreasson, Andr´e Treptow, and Tom Duckett. Selflocalization in non-stationary environments using omnidirectional vision. Robotics and Autonomous Systems, 55(7):541–551, 2007.
  30. Zhe Chen, Zhibin Hong, and Dacheng Tao. An experimental survey on correlation filter-based tracking. arXiv preprint arXiv:1509.05520, 2015.
  31. H. Collewign, C. J. Erkelens, and R. M. Steinman. Binocular co-ordination of human horizontal saccadic eye movements. Journal of Physiology, 404:157–182, oct 1988.
  32. Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. Online object tracking: A benchmark. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2411–2418, 2013.
  33. Robert Collins, Xuhui Zhou, and Seng Keat Teh. An open source tracking testbed and evaluation web site. In IEEE InternationalWorkshop on Performance Evaluation of Tracking and Surveillance, volume 35, 2005.
  34. Robert B Fisher. The pets04 surveillance ground-truth data sets. In Proc. 6th IEEE international workshop on performance evaluation of tracking and surveillance, pages 1–5, 2004.
  35. Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. Tracking-learning-detection. IEEE transactions on pattern analysis and machine intelligence, 34(7):1409–1422, 2012.
  36. Liang Li and Yi Luo. Improved video moving target tracking based on camshift. American Journal of Computational Mathematics, 6(04):357, 2016.
  37. Santhosh K Ramakrishnan, Swarna Kamlam Ravindran, and Anurag Mittal. Comal tracking: Tracking points at the object boundaries. arXiv preprint arXiv:1706.02331, 2017.
  38. Peter Ochs, Jitendra Malik, and Thomas Brox. Segmentation of moving objects by long term video analysis. IEEE transactions on pattern analysis and machine intelligence, 36(6):1187–1200, 2014.
  39. Sen-Ching S Cheung and Chandrika Kamath. Robust techniques for background subtraction in urban traffic video. In Proceedings of SPIE, volume 5308, pages 881–892, 2004.
  40. Zheng Han, Rui Zhang, Linru Wen, Xiaoyi Xie, and Zhijun Li. Moving object tracking method based on improved camshift algorithm. In Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), 2016 International Conference on, pages 91–95. IEEE, 2016.
  41. Ritesh Boda and Jasmine Pemeena Priyadarsini. Face detection and tracking using klt and viola jones. In ARPN, volume 11, pages 13472–13476, 2016.
  42. David S Bolme, J Ross Beveridge, Bruce A Draper, and Yui Man Lui. Visual object tracking using adaptive correlation filters. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2544–2550. IEEE, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Visual tracking visual machine surveillance