Texture Extraction in Video Tracking with Comparison of LBP and DWT

Authors
M. Sangam, Niranjan Lal, Manoj Diwakar
Corresponding Author
M. Sangam
Available Online April 2013.
Abstract
Object tracking using mean shift algorithm based on similarity between features of target region and candidate region. Candidate region is the current frame of tracking process. For similarity calculation color histogram and texture features are used. For texture feature extraction there are two methods LBP and discrete wavelet transform. In this paper comparison between these two approaches is done. Texture feature of target and candidate frame are extracted and integrated with color histogram. These features are together used for estimating the maximum similar candidate area with target area. This paper compares two methods of texture feature extraction. One is local binary pattern (LBP) and other one is discrete wavelet transform (DWT). These methods are compared on the basis of their computing time, performance and implementation.
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Proceedings
Proceedings of the Conference on Advances in Communication and Control Systems-2013
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2013
ISBN
978-90-78677-66-6
ISSN
1951-6851
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - M. Sangam
AU  - Niranjan Lal
AU  - Manoj Diwakar
PY  - 2013/04
DA  - 2013/04
TI  - Texture Extraction in Video Tracking with Comparison of LBP and DWT
BT  - Proceedings of the Conference on Advances in Communication and Control Systems-2013
PB  - Atlantis Press
SP  - 369
EP  - 374
SN  - 1951-6851
UR  - https://www.atlantis-press.com/article/6337
ID  - Sangam2013/04
ER  -