Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

@article{Li2018LearningSR,
  title={Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking},
  author={Feng Li and Cheng Tian and Wangmeng Zuo and Lei Zhang and Ming-Hsuan Yang},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={4904-4913}
}
Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue by enforcing spatial penalty on DCF coefficients, which, inevitably, improves the tracking performance at the price of increasing complexity. To tackle online updating, SRDCF formulates its model on multiple training images, further adding difficulties in improving efficiency. In this work, by introducing… 

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