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Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area

by Pushpa D., H. S. Sheshadri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 10
Year of Publication: 2012
Authors: Pushpa D., H. S. Sheshadri
10.5120/4855-7125

Pushpa D., H. S. Sheshadri . Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area. International Journal of Computer Applications. 39, 10 ( February 2012), 14-17. DOI=10.5120/4855-7125

@article{ 10.5120/4855-7125,
author = { Pushpa D., H. S. Sheshadri },
title = { Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 10 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number10/4855-7125/ },
doi = { 10.5120/4855-7125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:04.842092+05:30
%A Pushpa D.
%A H. S. Sheshadri
%T Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 10
%P 14-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The proposed system highlights a novel approach of detecting and tracking multiple objects in the cluttered area like crowd using greedy algorithm. The proposed framework uses position of traced low-level feature points to generate a group of autonomous rational mobility region as resultant. Various challenging factors towards the accuracy of detection rate for multiple objects are considered. The proposed approach has detected all the feasible rational mobile regions and extorts the sub-group which increases a total likelihood function along with assignment of each traced locus to one mobile region. Performance analysis is carried out with different set of video sequences to find that proposed system has gradual robust detection rate as well as highly cost-effective computationally

References
  1. Fei Yin, Dimitrios Makris, Sergio Velastin, Performance Evaluation of Object Tracking Algorithms, In 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS2007), Rio de Janeiro, Brazil (October 2007)
  2. Max Bajracharya, Baback Moghaddam, Andrew Howard, Shane Brennan, Larry H. Matthies, Results from a Real-time Stereo-based Pedestrian Detection System on a Moving Vehicle, Proceedings of the IEEE ICRA 2009 Workshop on People Detection and Tracking Kobe, Japan, May 2009
  3. Jifeng Ning, Lei Zhang, David Zhang, Chengke Wu, Robust object tracking using joint color-texture histogram, International Journal of Pattern Recognition and Artificial Intelligence Vol. 23, No. 7 (2009)
  4. Niels Willemsa, Willem Robert van Hageb, Gerben de Vriesc, Jeroen H.M. Janssensd, Veronique Malaiseb, An integrated approach for visual analysis of a multi-source moving objects knowledge base, International Journal of Geographical Information Science Vol. 24, No. 9, September 2010, 1–16
  5. Pabboju Sateesh Kumar, Multi-agent tracking under occlusion and 3D motion interpretation, Doctorial Thesis, Aug-2006
Index Terms

Computer Science
Information Sciences

Keywords

Component object detection greedy algorithm feature selection

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