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Object Tracking Based on Sparse Discriminative and Generative Model

S.M.R. Devi1

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-11 , Page no. 61-68, 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 , “Object Tracking Based on Sparse Discriminative and Generative Model,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.61-68, 2016.

MLA Style Citation: S.M.R. Devi "Object Tracking Based on Sparse Discriminative and Generative Model." International Journal of Computer Sciences and Engineering 4.11 (2016): 61-68.

APA Style Citation: S.M.R. Devi , (2016). Object Tracking Based on Sparse Discriminative and Generative Model. International Journal of Computer Sciences and Engineering, 4(11), 61-68.

BibTex Style Citation:
@article{Devi_2016,
author = {S.M.R. Devi },
title = {Object Tracking Based on Sparse Discriminative and Generative Model},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2016},
volume = {4},
Issue = {11},
month = {11},
year = {2016},
issn = {2347-2693},
pages = {61-68},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1109},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1109
TI - Object Tracking Based on Sparse Discriminative and Generative Model
T2 - International Journal of Computer Sciences and Engineering
AU - S.M.R. Devi
PY - 2016
DA - 2016/11/29
PB - IJCSE, Indore, INDIA
SP - 61-68
IS - 11
VL - 4
SN - 2347-2693
ER -

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Abstract

Real time object tracking is a challenging task in computer vision. Many algorithms exist in literature like mean shift method, kernel method , pixel based, Silhouette based and sparity based, method. Of these methods robust appearance model that exploits both holistic templates and local representations is the sparsity-based discriminative classifier (SDC) and a sparsity-based generative model (SGM). SDC module, is effective method to compute the confidence value that assigns more weights to the foreground than the background in the SGM module. Further the histogram-based method is also discussed that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively. Experimental results show that the above method gives good performance and accuracy even in the presence of occlusion.

Key-Words / Index Term

Object tracking, Target feature modelling, sparsity-based generative model, sparsity-based discriminative classifier

References

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