Learning Rotation for Kernel Correlation Filter

@article{Hamdi2017LearningRF,
  title={Learning Rotation for Kernel Correlation Filter},
  author={Abdullah Hamdi and Bernard Ghanem},
  journal={CoRR},
  year={2017},
  volume={abs/1708.03698}
}
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate… CONTINUE READING
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End-to-end representation learning for correlation filter based tracking, 2017

  • J. Valmadre
  • 2017
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