Object Tracking via 2DPCA and l2-Regularization

Abstract EN

We present a fast and robust object tracking algorithm by using 2DPCA and l2-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 l2-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.