Learning Background-Aware Correlation Filters for Visual Tracking

@article{Galoogahi2017LearningBC,
  title={Learning Background-Aware Correlation Filters for Visual Tracking},
  author={Hamed Kiani Galoogahi and Ashton Fagg and Simon Lucey},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1144-1152}
}
Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this… CONTINUE READING
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