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Experimental analysis of Mean shift method of tracking objects

S.M.R. Devi1

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-11 , Page no. 7-12, Nov-2016

Online published on Nov 29, 2016

Copyright © S.M.R. Devi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S.M.R. Devi, “Experimental analysis of Mean shift method of tracking objects,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.7-12, 2016.

MLA Style Citation: S.M.R. Devi "Experimental analysis of Mean shift method of tracking objects." International Journal of Computer Sciences and Engineering 4.11 (2016): 7-12.

APA Style Citation: S.M.R. Devi, (2016). Experimental analysis of Mean shift method of tracking objects. International Journal of Computer Sciences and Engineering, 4(11), 7-12.

BibTex Style Citation:
@article{Devi_2016,
author = { S.M.R. Devi},
title = {Experimental analysis of Mean shift method of tracking objects},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2016},
volume = {4},
Issue = {11},
month = {11},
year = {2016},
issn = {2347-2693},
pages = {7-12},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1096},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1096
TI - Experimental analysis of Mean shift method of tracking objects
T2 - International Journal of Computer Sciences and Engineering
AU - S.M.R. Devi
PY - 2016
DA - 2016/11/29
PB - IJCSE, Indore, INDIA
SP - 7-12
IS - 11
VL - 4
SN - 2347-2693
ER -

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Abstract

Real time object tracking is a perplexing task in computer vision. Many algorithms exist in literature like Mean shift, background-weighted histogram (BWH) and Corrected background-weighted histogram(CBWH) for tracking the moving objects in a video sequence.This paper attempts to do the comparative analysis of the three methods in terms of performance parameters like Normalised Centroid Distance , Overlap and number of iterations using two types of features i.e., color histogram and color texture histogram. Experimental results show that the performance of CBWH gives better performance when compared with basic Mean shift and BWH.

Key-Words / Index Term

Object Tracking, Mean Shift Algorithm, Target Feature Modelling, Candidate Feature Modelling, Bhattacharya Coefficients

References

[1] Babenko B, Yang M H, Belongie S. "Robust object tracking with online multiple instance learning", IEEE Transactions on Pattern Analysis and Machine Intelligence" 2011,33 (8): 1619- 1632.
[2] Gandham. Sindhuja, Renuka Devi S.M.: "A Survey on detection and tracking of objects in a Video sequence", International Journal of Engineering Research and General Science Volume 3, Issue 2, Part 2, March-April, 2015, pp.418-426.
[3] K. Fukunaga, L.D. Hostetler, "The Estimation of the Gradient of a Density Function with Application in Pattern Recognition", IEEE Trans. Information Theory, vol. 21, no. 1, pp. 32-40, Jan. 1975.
[4] Saravanakumar, S. Vadivel and A. Saneem Ahmed, "Multiple human object tracking using background subtraction and shadow removal techniques", The International Conference on Signal and Image Processing ,2010, pp. 15-17.
[5] A. Yimaz, O. Javed and M. Shah, "Object tracking: A survey," ACM Computing Surveys, Vol. 38, No. 4, Article 13, December 2006, pp. 13-20.
[6] Gandham Sindhuja, Renuka Devi S.M. Comparative analysis of mean shift in object tracking. IEEE Conference on Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG),2015, 283-287.
[7] Meng G, Jiang G, "Real-time illumination robust maneuvering target tracking based on color invariance", Proceedings of the 2nd International Congress on Image and Signal, 2009, pp. 15.
[8] Gammeter S, Bossard L, Gassmann A, et al. "Server-side object recognition and client-side object tracking for mobile augmented reality", CVPR, IEEE Computer Society Conference, 20 I 0: 1-8.
[9] N. A Gmez. "A Probabilistic Integrated Object Recognition and Tracking Framework for Video Sequences", University at Rovira I Virgili, PHD thesis, Espagne, 2009.
[10] Collins,R T, Yanxi Liu and Leordeanu, M, "Online selection of discriminative tracking features", IEEE Transactions on Pattern Analysis and Machine Intelligence , 2010, Vol. 10, pp. 1631-1643.
[11] Ying-JiaYeh, Chiou-Ting Hsu, "Online Selection of Tracking Features Using AdaBoost", IEEE Transactions on Circuits and Systems for Video Technology, 2009, VoU, pp. 442-446.
[12] Comaniciu D., Ramesh V. and Meer P.: �Kernel-Based Object Tracking�, IEEE Trans. On Pattern Anal. Machine Intell., 2003, 25, (2), pp. 564-577.
[13] Ning, Lei Zhang, David Zhang and C. Wu, "Robust Mean Shift Tracking with Corrected Background-Weighted Histogram," to appear in lET Computer Vision.(2011).
[14] D. Comaniciu and P. Meer. Mean shift: "A robust approach toward feature space analysis". PAMI,24(5):603-619, 2002.
[15] K. Nummiaro, E. Koller-Meier, and L. 1. Van Gool. "Object tracking with an adaptive color-based particle filter. In Proc. Of the 24th DAGM Symposium on Pattern Recognition, pages 353-360, London, UK, 2002. Springer-Verlag.
[16] Ess A, Schindler K, Leibe B, "Object detection and tracking for autonomous navigation in dynamic environments". IJRR, 2010, 29(14): 1707-1725.
[17] Shah M, Saleemi I, Hartung L, "Scene understanding by statistical modeling of motion patterns", IEEE Conference CVPR, 2010: 2069-2076.
[18] Patel, Sandeep Kumar, and Agya Mishra. "Moving object racking techniques: A critical review." Indian Journal of Computer Science and Engineering 4.2 (2013): 95-102.
[19] ]cmp.felk.cvut.czl-vojirtom/datasetl, www.iai.unibonn.de/-kleindltracking. clickdamage.coml..Jcv _ datasets.php
[20] Ning J, Zhang L, Zhang D, et al. "Robust object tracking using joint color-texture histogram." International Journal of Pattern Recognition and Artificial Intelligence, 2009,23(07): 1245-1263.
[21] Pietik�inen, Matti, et al. "Local binary patterns for still images." Computer vision using local binary patterns. Springer London, 2011. 13-47.