Abstract

Most existing appearance models for visual tracking usually construct a pixel-based representation of object appearance so that they are incapable of fully capturing both global and local spatial layout information of object ap- pearance. In order to address this problem, we propose a novel spatial Log- Euclidean appearance model (referred as SLAM ) under the recently introduced Log-Euclidean Riemannian metric [23]. SLAM is capable of capturing both the global and local spatial layout information of object appearance by constructing a block-based Log-Euclidean eigenspace representation. Speci fically, the process of learning the proposed SLAM consists of five steps—appearance block division, online Log-Euclidean eigenspace learning, local spatial weighting, global spatial weighting, and likelihood evaluation. Furthermore, a novel online Log-Euclidean Riemannian subspace learning algorithm (IRSL) [14] is applied to incrementally update the proposed SLAM. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed SLAM.