Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

@article{Liu2017AdaptiveCT,
  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},
  year={2017},
  volume={47},
  pages={4289-4301}
}
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
Highly Cited
This paper has 17 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 2 times over the past 90 days. VIEW TWEETS
6 Citations
43 References
Similar Papers

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 43 references

The visual object tracking vot2014 challenge results

  • M. Kristan
  • Proc. Eur. Conf. Comput. Vis. Workshops, vol…
  • 2014
Highly Influential
9 Excerpts

Similar Papers

Loading similar papers…