  We address Unsupervised Video Object Segmentation (UVOS), the task of
automatically generating accurate pixel masks for salient objects in a video
sequence and of tracking these objects consistently through time, without any
input about which objects should be tracked. Towards solving this task, we
present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking)
as a simple and generic algorithm which is able to track and segment a large
variety of objects. This algorithm builds up tracks in a number stages, first
grouping segments into short tracklets that are spatio-temporally consistent,
before merging these tracklets into long-term consistent object tracks based on
their visual similarity. In order to achieve this we introduce a novel
tracklet-based Forest Path Cutting data association algorithm which builds up a
decision forest of track hypotheses before cutting this forest into paths that
form long-term consistent object tracks. When evaluating our approach on the
DAVIS 2017 Unsupervised dataset we obtain state-of-the-art performance with a
mean J &F score of 67.9% on the val, 58% on the test-dev and 56.4% on the
test-challenge benchmarks, obtaining first place in the DAVIS 2019 Unsupervised
Video Object Segmentation Challenge. UnOVOST even performs competitively with
many semi-supervised video object segmentation algorithms even though it is not
given any input as to which objects should be tracked and segmented.
