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2007
IEEE

Robust Visual Tracking Based on Incremental Tensor Subspace Learning

11 years 3 months ago
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 ...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang,
Added 14 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Guan Luo
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