Abstract:
The principal component analysis based on
L2-norm (
L2-PCA) is sensitive to outliers, which result in the visual tracking algorithms based on
L2-PCA having lower robustness to occlusions.To alleviate this problem, a novel visual tracking algorithm via
L1-norm maximization principal component analysis (PCA-
L1) is proposed in this paper.The proposed algorithm models the object appearance using PCA-
L1, and infers the states of object with particle filter.In addition, to adapt to changes of object appearance and avoid model drifting, an online PCA-
L1 update method is proposed.The experimental results on several challenging sequences show that the proposed algorithm has better performance than that of the state-of-the-art tracker.