@inproceedings{a9e96bc0d1ad4a35b10d447f10cadcb1,
title = "Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters",
abstract = "Standard RGB-D trackers treat the target as a 2D structure, which makes modelling appearance changes related even to out-of-plane rotation challenging. This limitation is addressed by the proposed long-term RGB-D tracker called OTR – Object Tracking by Reconstruction. OTR performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance- enhancing features: (i) generation of an accurate spatial support for constrained DCF learning from its 2D projection and (ii) point-cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation on the Princeton RGB-D tracking and STC Benchmarks shows OTR outperforms the state-of-the-art by a large margin.",
author = "Ugur Kart and Alan Lukezic and Matej Kristan and Joni-Kristian K{\"a}m{\"a}r{\"a}inen and Jiri Matas",
note = "EXT={"}Matas, Jiri{"}; IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR ; Conference date: 01-01-2000",
year = "2019",
month = jun,
language = "English",
isbn = "978-1-7281-3294-5",
series = "IEEE/CVF Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE",
booktitle = "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
address = "United States",
}