Recovering background regions in videos of cluttered urban scenes
Iain Rodger, Barry Connor, Neil M. Robertson · 2015
In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.