  This paper describes an unsupervised machine learning methodology capable of
target tracking and background suppression via a novel dual-model approach.
``Jekyll`` produces a video bit-mask describing an estimate of the locations of
moving objects, and ``Hyde`` outputs a pseudo-background frame to subtract from
the original input image sequence. These models were trained with a
custom-modified version of Cross Entropy Loss.
  Simulated data were used to compare the performance of Jekyll and Hyde
against a more traditional supervised Machine Learning approach. The results
from these comparisons show that the unsupervised methods developed are
competitive in output quality with supervised techniques, without the
associated cost of acquiring labeled training data.
