Object tracking using dimension reduction of descriptive features
Lin C.; Pun C.-M.
Source PublicationProceedings - 2014 11th International Conference on Computer Graphics, Imaging and Visualization: New Techniques and Trends, CGiV 2014
AbstractIn this paper, we proposed a novel feature refining method for object tracking using vectorized texture feature. Our contributions are three-fold: 1) an statistical discriminative appearance model using texture feature was proposed. 2) majority of dimensions of the features are removed by judging their errors of the chosen distribution model. The remaining dimensions are most discriminative ones for classification task. The dimension reduction has advantages of reducing the computational cost in classification stage. 3) an adaptive learning rate was proposed to handle drifts caused by long term occlusion. Experimental results are satisfactory and compared to state-of-the-art object tracking methods.
Keywordadaptive learning rate feature refining object tracking texture feature
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Lin C.,Pun C.-M.. Object tracking using dimension reduction of descriptive features[C],2014:73-77.
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