Robust Visual Tracking Based on an Effective Appearance Model

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 appearance. In order to address this problem, we propose a novel spatial LogEuclidean 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. Specifically, 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 ...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang
• Keywords: Computer Vision, ECCV 2008, Global Spatial Weighting, Local Spatial Layout, Local Spatial Weighting, Spatial Layout Information, Spatial Logeuclidean Appearance