Iterative automated foreground segmentation in video sequences using graph cuts

Abstract

In this paper we propose a method for foreground object segmentation in videos using an improved version of the GrabCut algorithm. Motivated by applications in de-identification, we consider a static camera scenario and take into account common problems with the original algorithm that can result in poor segmentation. Our improvements are as follows: (i) using background subtraction, we build GMM-based segmentation priors; (ii) in building foreground and background GMMs, the contributions of pixels are weighted depending on their distance from the boundary of the object prior; (iii) probabilities of pixels belonging to foreground or background are modified by taking into account the prior pixel classification as well as its estimated confidence; and (iv) the smoothness term of GrabCut is modified by discouraging boundaries further away from the object prior. We perform experiments on CDnet 2014 Pedestrian Dataset and show considerable improvements over a reference implementation of GrabCut.