Nonlinear learning using LCC for online visual tracking

In this paper, we propose to address online visual tracking on the basis of Local Coordinate Coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the local coordinate codes of image p...

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Bibliographic Details
Published inProceedings (IEEE International Conference on Multimedia and Expo) pp. 1 - 6
Main Authors Hongwei Hu, Bo Ma, Tao Xu, Junbiao Pang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
Dictionaries
Encoding
Image reconstruction
local coordinate coding
manifold
Manifolds
nonlinear learning
Target tracking
visual tracking
Visualization
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ISSN1945-7871
DOI10.1109/ICME.2014.6890210

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Summary:In this paper, we propose to address online visual tracking on the basis of Local Coordinate Coding (LCC), which integrates the advantages of the discriminative method and the generative method. In the discriminative module, a nonlinear function is trained using the local coordinate codes of image patches to identify the foreground patches from background. In the generative module, we introduce a similarity function that takes the spatial structures of local patches in the target into account between the candidate and holistic templates by reconstruction error. To deal with appearance change during tracking, an online update method is introduced. The proposed tracking method is evaluated on different challenging video sequences with center location error, and experimental results demonstrate the good performance of our method.
ISSN:1945-7871
DOI:10.1109/ICME.2014.6890210