Robust visual tracking via guided low-rank subspace learning

@article{Wang2015RobustVT,
  title={Robust visual tracking via guided low-rank subspace learning},
  author={Di Wang and Risheng Liu and Zhixun Su},
  journal={2015 IEEE International Conference on Image Processing (ICIP)},
  year={2015},
  pages={1-5}
}
Subspace methods have attracted increasing attention for visual tracking. However, most previous work only aim to pursuit the subspace basis to represent appearances, thus cannot reveal the rich structure information in real world videos. This paper proposes a guided low-rank subspace learning framework to simultaneously extract the orthogonal subspace… CONTINUE READING