Earth-Mover's Distance as a Tracking Regularizer (bibtex)
by , , and
Abstract:
Tracking time-varying signals is an important part of many engineering systems. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. Leveraging sparsity, however, depends heavily on gridding the space, treating the signal as a collection of active or inactive pixels in an image, rather than traditional methods which track the continuous spatial coordinates. Using the dynamics constraint in this setting is challenging, as a model which approximately predicts target location may result in seemingly large errors, as measured by the lp-norm typically used in such algorithms. To take advantage of approximate spatial priors without introducing unnecessary penalties, we present a tracking algorithm using the earth-mover’s distance (EMD) as an alternate dynamics regularization term. We note that while requiring a higher computational burden, the EMD can more effectively utilize target location prediction when the space is gridded.
Reference:
Earth-Mover's Distance as a Tracking RegularizerA. Charles, N. Bertrand, J. Lee and C.J. Rozell. In IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), December 2017. Submitted.
Bibtex Entry:
@InProceedings{charles.17c,
  author = 	 {Charles, A. and Bertrand, N. and Lee, J. and Rozell, C.J.},
  title = 	 {{Earth-Mover's Distance} as a Tracking Regularizer},
  booktitle =	 {{IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)}},
  year =	 2017,
  month = dec,
  address =	 {Cura\c{c}ao, Dutch Antilles},
  abstract = {Tracking time-varying signals is an important part of many engineering systems. Recently, signal processing techniques have been developed to improve tracking performance when the signal of interest is known a-priori to be sparse. Leveraging sparsity, however, depends heavily on gridding the space, treating the signal as a collection of active or inactive pixels in an image, rather than traditional methods which track the continuous spatial coordinates. Using the dynamics constraint in this setting is challenging, as a model which approximately predicts target location may result in seemingly large errors, as measured by the lp-norm typically used in such algorithms. To take advantage of approximate spatial priors without introducing unnecessary penalties, we present a tracking algorithm using the earth-mover’s distance (EMD) as an alternate dynamics regularization term. We note that while requiring a higher computational burden, the EMD can more effectively utilize target location prediction when the space is gridded.},
  note = {Submitted.}
}
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