Object Tracking via 2DPCA and ℓ_2-Regularization

We present a fast and robust object tracking algorithm by using 2DPCA and l 2 -regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l 2 -re...

Full description

Saved in:
Bibliographic Details
Published inJournal of Electrical and Computer Engineering Vol. 2016; pp. 849 - 855
Main Authors Wang, Haijun, Ge, Hongjuan, Zhang, Shengyan
Format Journal Article
LanguageEnglish
Published Hindawi Limiteds 2016
Online AccessGet full text

Cover

More Information
Summary:We present a fast and robust object tracking algorithm by using 2DPCA and l 2 -regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l 2 -regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.
ISSN:2090-0147
2090-0155
DOI:10.1155/2016/7975951