Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

  title={Adaptive Compressive Tracking via Online Vector Boosting Feature Selection},
  author={Qingshan Liu and Jing Yang and Kaihua Zhang and Yi Wu},
  journal={IEEE Transactions on Cybernetics},
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper, we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects. First… CONTINUE READING
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