L2-RLS-Based Object Tracking

In this paper, we present a robust and fast tracking algorithm in which object tracking is achieved by solving ℓ₂-regularized least square (ℓ₂-RLS) problems in a Bayesian inference framework. First, the changing appearance of the tracked target is modeled with PCA basis vectors and square templates, which makes the tracker not only exploit the strength of subspace representation but also explicitly take partial occlusion into consideration. They can together represent both the intact and corrupted objects well. Second, we adopt the ℓ₂-regularized least square method to solve the proposed representation model. Compared with the complex ℓ₁-based algorithm, it provides a very fast performance without the loss of accuracy in handling the tracking problem. In addition, a novel likelihood function and a refined update scheme further help to improve the robustness of our tracker. Both qualitative and quantitative evaluations on several challenging image sequences demonstrate that the proposed method performs favorably against several state-of-the-art tracking algorithms.