UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking for the 2019 Unsupervised DAVIS Challenge

Idil Esen Zulfikar, Jonathon Luiten, Bastian Leibe
The 2019 DAVIS Challenge on Video Object Segmentation - CVPR Workshops

We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixelmasks for salient objects in a video sequence and of track-ing these objects consistently through time, without any in-formation about which objects should be tracked. Towardssolving this task, we present UnOVOST (Unsupervised Of-fline Video Object Segmentation and Tracking) as a simpleand generic algorithm which is able to track a large varietyof objects. This algorithm hierarchically builds up tracksin five stages. First, object proposal masks are generatedusing Mask R-CNN. Second, masks are sub-selected andclipped so that they do not overlap in the image domain.Third, tracklets are generated by grouping object propos-als that are strongly temporally consistent with each otherunder optical flow warping. Fourth, tracklets are mergedinto long-term consistent object tracks using their temporalconsistency and an appearance similarity metric calculatedusing an object re-identification network. Finally, the mostsalient object tracks are selected based on temporal tracklength and detection confidence scores. We evaluate ourapproach on the DAVIS 2017 Unsupervised dataset and ob-tain state-of-the-art performance with a meanJ&Fscoreof 58% on the test-dev benchmark. Our approach furtherachieves first place in the DAVIS 2019 Unsupervised VideoObject Segmentation Challenge with a mean ofJ&Fscoreof 56.4% on the test-challenge benchmark.

» Show BibTeX

title={UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking for the 2019 Unsupervised DAVIS Challenge},
author={Zulfikar, Idil Esen and Luiten, Jonathon and Leibe, Bastian}

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