Generalized pooling for robust object tracking

Bo Ma, Hongwei Hu, Jianbing Shen, Yangbiao Liu, Ling Shao

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
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Feature pooling in a majority of sparse coding-based tracking algorithms computes final feature vectors only by low-order statistics or extreme responses of sparse codes. The high-order statistics and the correlations between responses to different dictionary items are neglected. We present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes. Since immediate matching between two distributions usually requires high computational costs, we introduce the Fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target. We encode target patches by local coordinate coding, utilize Gaussian mixture model to compute Fisher vectors, and finally train semi-supervised linear kernel classifiers for visual tracking. In order to handle the drifting problem during the tracking process, these classifiers are updated online with current tracking results. The experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state-of-the-art tracking algorithms.
Original languageEnglish
Pages (from-to)4199-4208
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number9
Early online date7 Jul 2016
Publication statusPublished - 1 Sep 2016

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