Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals
Citations Over TimeTop 1% of 2016 papers
Abstract
We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals-spatio-temporal tubes that localize the foreground objects-in an unannotated video. In contrast to previous unsupervised methods that either track regions initialized in an arbitrary frame or train a fixed model over a cluster of regions, we instead discover a set of easy-togroup instances of an object and then iteratively update its appearance model to gradually detect harder instances in temporally-adjacent frames. Our method first generates a set of spatio-temporal bounding box proposals, and then refines them to obtain pixel-wise segmentation proposals. We demonstrate state-of-the-art segmentation results on the SegTrack v2 dataset, and bounding box tracking results that perform competitively to state-of-the-art supervised tracking methods.
Related Papers
- → Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation(2020)30 cited
- → Medical Image Segmentation with Imperfect 3D Bounding Boxes(2021)1 cited
- → Bounding-box Centralization for Improving SiamFC++(2021)1 cited
- A Method to Generate the Minimum Bounding Boxes for Shape-Arbitrary Objects(2010)