@inproceedings{9ac8e847dd184b90b7c72ea3b5e811a0,
title = "REVT: Robust and Efficient Visual Tracking by Region-Convolutional Regression Network",
abstract = "This paper proposes a novel approach, namely REVT, for visual tracking based on a region convolutional regression network. REVT runs according to a coarse to fine scheme. It first builds an on-line update deep network to roughly select a candidate region in a fast way. It then refines the result by exquisitely searching the target within the candidate region by a deep regression network, which is trained off-line to account for more diverse intra-class appearance changes. REVT thus integrates the advantages of the two types of deep models, and demonstrates a good trade-off between accuracy and efficiency. We perform extensive experiments on the OTB-2013 and OTB-2015 benchmarks, and REVT reports competitive performance at a speed of 19 fps, proving its competency.",
keywords = "Coarse-to-fine, Deep regression network, Visual tracking",
author = "Peng Wu and Di Huang and Yunhong Wang",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 24th International Conference on MultiMedia Modeling, MMM 2018 ; Conference date: 05-02-2018 Through 07-02-2018",
year = "2018",
doi = "10.1007/978-3-319-73603-7\_36",
language = "英语",
isbn = "9783319736020",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "440--452",
editor = "Klaus Schoeffmann and Moncef Gabbouj and O'Connor, \{Noel E.\} and Ahmed Elgammal and Chalidabhongse, \{Thanarat H.\} and Supavadee Aramvith and Ngo, \{Chong Wah\} and Yo-Sung Ho",
booktitle = "MultiMedia Modeling - 24th International Conference, MMM 2018, Proceedings",
address = "德国",
}