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A System for Dissecting the Video for Tracing Multiple Humans in Multifaceted Situation

by M. Hemalatha, V. Vinodhini, B. Sivaranjani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 5
Year of Publication: 2013
Authors: M. Hemalatha, V. Vinodhini, B. Sivaranjani
10.5120/13390-1030

M. Hemalatha, V. Vinodhini, B. Sivaranjani . A System for Dissecting the Video for Tracing Multiple Humans in Multifaceted Situation. International Journal of Computer Applications. 77, 5 ( September 2013), 16-20. DOI=10.5120/13390-1030

@article{ 10.5120/13390-1030,
author = { M. Hemalatha, V. Vinodhini, B. Sivaranjani },
title = { A System for Dissecting the Video for Tracing Multiple Humans in Multifaceted Situation },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 5 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 16-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number5/13390-1030/ },
doi = { 10.5120/13390-1030 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:27.938822+05:30
%A M. Hemalatha
%A V. Vinodhini
%A B. Sivaranjani
%T A System for Dissecting the Video for Tracing Multiple Humans in Multifaceted Situation
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 5
%P 16-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmenting and tracking multiple humans is a challenging problem in complex situations in which extended occlusion, shadow and/or reflection exists. We tackle this problem with a 3D model-based approach. This method includes two stages, segmentation (detection) and tracking. Human hypotheses are generated by shape analysis of the foreground blobs using human shape model. The segmented human hypotheses are tracked with a Kalman filter with explicit handling of occlusion. Hypotheses are verified while they are tracked for the first second or so. The verification is done by walking recognition using an articulated human walking model. We propose a new method to recognize walking using motion template and temporal integration. Experiments show that our approach works robustly in very challenging Sequences.

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Index Terms

Computer Science
Information Sciences

Keywords

Human Tracking Segmentation Kalman Filter Motion Template.

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