Visual Tracking via L₁-Norm Maximization Principal Component Analysis

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.