Latent Subspace Projection Pursuit with Online Optimization for Robust Visual Tracking

This article develops a novel subspace learning algorithm for visual tracking. Specifically, the authors first present a linear projection view to formulate subspace learning and then develop a novel framework, called Latent Subspace Projection Pursuit (LSPP), to estimate the intrinsic dimension, removing corruptions and recovering the subspace structure for observed datasets. The authors evaluate the performance of their proposed method on various synthetic and real-world datasets, and the experimental results demonstrate that LSPP can achieve significant improvements in terms of performance and reduced computational complexity for visual tracking.