Exploring the Combination of PReMVOS, BoLTVOS and UnOVOST for the 2019 YouTube-VOS Challenge

Video Object Segmentation is the task of tracking and segmenting objects in a video given the first-frame mask of objects to be tracked. There have been a number of different successful paradigms for tackling this task, from creating object proposals and linking them in time as in PReMVOS, to detecting objects to be tracked conditioned on the given first-frame as in BoLTVOS, and creating tracklets based on motion consistency before merging these into long-term tracks as in UnOVOST. In this paper we explore how these three different approaches can be combined into a novel Video Object Segmentation algorithm. We evaluate our approach on the 2019 Youtube-VOS challenge where we obtain 6th place with an overall score of 71.5%.