Robust Visual Tracking Based on Incremental Tensor Subspace Learning

Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspace analysis is incorporated into the multilinear framework which offline constructs a representation of image ensembles using high-order tensors. This reduces spatio-temporal redundancies substantially, whereas the computational and memory cost is high. In this paper, we present an effective online tensor subspace learning algorithm which models the appearance changes of a target by incrementally learning a low-order tensor eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking then is led by the state inference within the framework in which a particle filter is used for propagating sample distributions over the time. A novel likelihood function, based on the tensor reconstruction error norm, is developed to measure the similarity between the test image and the learned ...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Guan Luo
• Keywords: Computer Vision, ICCV 2007, Low-order Tensor Eigenspace, Online Tensor Subspace, Subspace Analysis-based Tracking, Tensor Reconstruction Error, Tensor Subspace Model