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Reseach Article

Efficient Method for Detection and Tracking of People

Published on April 2012 by S. Anantha Jothi, K. Thulasimani
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 4
April 2012
Authors: S. Anantha Jothi, K. Thulasimani
ae241725-b6a2-4263-b211-dd92c3f5fbc6

S. Anantha Jothi, K. Thulasimani . Efficient Method for Detection and Tracking of People. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 4 (April 2012), 15-18.

@article{
author = { S. Anantha Jothi, K. Thulasimani },
title = { Efficient Method for Detection and Tracking of People },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 4 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/icon3c/number4/6027-1028/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A S. Anantha Jothi
%A K. Thulasimani
%T Efficient Method for Detection and Tracking of People
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 4
%P 15-18
%D 2012
%I International Journal of Computer Applications
Abstract

This paper aims to develop an effective method to estimate the human in a frame of video stream. The paper has been threefold, first the preprocessing steps are performed using background subtraction results in foreground extraction and need training images to determine the relationship between foreground pixels and human oriented feature, second an Expectation Maximization based method has been used to cluster individuals in a low resolution scene. The cluster model is used to represent each person. Third the number of people is used as a priori for locating individuals based on feature points. The KLT tracker is used to track the people. Then the methods for estimating the number of people and for locating individuals are connected. Finally, a model is constructed to test the proposed system. Evaluation results on a number of images and videos and comparisons with previous methods are given.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Expectation And Maximization Cluster Kanade Lucas Tracker