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Journal of Electrical and Computer Engineering
Volume 2016, Article ID 7975951, 7 pages
Research Article

Object Tracking via 2DPCA and -Regularization

1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2Aviation Information Technology R & D Center, Binzhou University, Binzhou 256603, China

Received 10 March 2016; Accepted 13 July 2016

Academic Editor: Jiri Jan

Copyright © 2016 Haijun Wang et al. 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.


We present a fast and robust object tracking algorithm by using 2DPCA and -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 -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.