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Reseach Article

A New Methodology for Crowd Estimation: Linear Quadratic Estimation

by Jugal Kishor Gupta, S.K. Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 7
Year of Publication: 2015
Authors: Jugal Kishor Gupta, S.K. Gupta
10.5120/ijca2015906370

Jugal Kishor Gupta, S.K. Gupta . A New Methodology for Crowd Estimation: Linear Quadratic Estimation. International Journal of Computer Applications. 127, 7 ( October 2015), 37-40. DOI=10.5120/ijca2015906370

@article{ 10.5120/ijca2015906370,
author = { Jugal Kishor Gupta, S.K. Gupta },
title = { A New Methodology for Crowd Estimation: Linear Quadratic Estimation },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 7 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number7/22745-2015906370/ },
doi = { 10.5120/ijca2015906370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:17.740785+05:30
%A Jugal Kishor Gupta
%A S.K. Gupta
%T A New Methodology for Crowd Estimation: Linear Quadratic Estimation
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 7
%P 37-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, crowd estimation techniques in real-time are more popular research field using computer vision. here understand the behavior of the system using Linear Quadratic Estimation or kalman filter with new proposed index parameter which will help to understand the accuracy of the system still no more parameter discover to judge the accuracy of the system which is us to estimate the crowd or tracking the crowd. Crowd estimation does play an very critical role in intelligent crowd monitoring. All results have been implemented in MATLAB R2013.

References
  1. Beril Sirmacek, Peter Reinartz, Kalman Filter Based Feature Analysis For Tracking People From Airborne Images, German Aerospace Center (DLR), Remote Sensing Technology Institute PO Box 1116, 82230, Wessling, Germany
  2. Ming Jiang , Jingcheng Huang , Xingqi Wang , Jingfan Tang , Chunming Wu, An Approach for Crowd Density and Crowd Size Estimation ,JOURNAL OF SOFTWARE, VOL. 9, NO. 3, MARCH 2014.
  3. A.N. Marana, S.A. Velastin, L.F. Costa, R.A.Lotufo, Automatic Estimation of Crowd Density Using Texture”, International Workshop on Systems and Image Processing, IWSIP’97, May 28-30, Poland.
  4. Mohamed H. Dridi, "Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009",Current Urban Studies, 2015, 3, 35-53 Published Online March 2015 in SciRes.
  5. Shilin Zhang and Xunyuan Zhang “Crowded Pedestrian Detection and Density Estimation by Visual Words Analysis”, International Journal of Multimedia and Ubiquitous Engineering Vol. 10, No. 3 (2015), pp. 99-108 http://dx.doi.org/10.14257/ijmue.2015.10.3.10.
  6. T.S.Surendiran , G.Michael, S.P.Vijayaragavan “Crowd Density Estimation Using Accumulated Mosaic Image Difference” International Journal Of Engineering Sciences & Research Technologyissn: 2277-9655 Scientific Journal Impact Factor: 3.449 (Isra), Impact Factor: 2.114.
  7. Kai Arras, Cyrill Stachniss, Maren Bennewitz, Wolfram Burgard, Robotics 2 Target Tracking , Gian Diego Tipaldi, v.1.1, Jan 2012.
  8. Schofer J. Ushpiz A. Polus, A. Pedestrian Flow and Level of Service. J. Transportation Eng,109(1):46-56, 1983.
  9. Zi Ye, Jinqiao Wang, Zhenchong Wang, Hanqing Lu. Multiple features fusion for crowd density estimation. Proceeding ICIMCS '12 Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, pp. 42-45, 2012.
  10. Subburaman V B, Descamps A, Carincotte C. Counting People in the Crowd Using a Generic Head Detector[C]. Proceedings of 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance (AVSS): September 18-21, 2012. Beijing, China, pp. 470-475, 2012.
  11. J.F. Dickie, “Major crowd catastrophes”, Safety Science, vol. 18, pp. 309-320, 1995
  12. Kumar, T. and K. Verma, 2010a. A theory based on conversion of RGB image to gray image. Int. J. Computer. Appli., 7: 5-12. DOI: 10.5120/1140-1493.
  13. J.D. Sime, “Crowd psychology and engineering”, Safety Science, vol. 21, pp. 1-14, 1995.
  14. R.C. Gonzales, R. Woods, Digital Image Processing, Addison-Wesley Publishing Company, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

crowd filter parameter kalman and Linear Quadratic Estimation.