Probabilistic Framework for Segmenting People Under Occlusion

  title={Probabilistic Framework for Segmenting People Under Occlusion},
  author={Ahmed M. Elgammal and Larry S. Davis},
In this paper we address the problem of segmenting foreground regions corresponding to a group of people given models of their appearance that were initialized before occlusion. We present a general framework that uses maximum likelihood estimation to estimate the best arrangement for people in terms of 2D translation that yields a segmentation for the foreground region. Given the segmentation result we conduct occlusion reasoning to recover relative depth information and we show how to utilize… CONTINUE READING
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