  The paper addresses the problem of motion saliency in videos, that is,
identifying regions that undergo motion departing from its context. We propose
a new unsupervised paradigm to compute motion saliency maps. The key ingredient
is the flow inpainting stage. Candidate regions are determined from the optical
flow boundaries. The residual flow in these regions is given by the difference
between the optical flow and the flow inpainted from the surrounding areas. It
provides the cue for motion saliency. The method is flexible and general by
relying on motion information only. Experimental results on the DAVIS 2016
benchmark demonstrate that the method compares favourably with state-of-the-art
video saliency methods.
