Robust Object Tracking Based on Collaborative Model via L2-Norm Minimization

Abstract

The computational cost of the tracking algorithms with sparse representation is relatively large, however, we proposed a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representation to account for drastic appearance model, properly solved by the $l_{2}$ norm minimization solution in a Bayesian inference framework, which is proved to be effective and efficient. In the process of the object tracking process, the positive template and negative template of the discriminant model together with the coefficient of the generative model are timely updated so as to have a strong adaptability and robust discrimination. In the discriminative module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In order to speed up the tracking algorithm, a variance particle filter algorithm is proposed to avoid the computational load of the particles with low similarity. Experiments on some challenge video sequences demonstrate that our proposed tracker is robust and effective to challenge issues such as illumination change, clutter background partial occlusion and so on and perform favorably against state-of-art algorithms.