We propose FuCoLoT -- a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently. FuCoLoT maintains several correlation filters trained on different time scales that act as the detector components. A novel mechanism based on the correlation response is used for tracking failure estimation. FuCoLoT achieves state-of-the-art results on standard short-term benchmarks and it outperforms the current best-performing tracker on the long-term UAV20L benchmark by over 19%. It has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15fps in a single CPU thread.
@article{arxiv.1711.09594,
title = {FuCoLoT -- A Fully-Correlational Long-Term Tracker},
author = {Alan Lukežič and Luka Čehovin Zajc and Tomáš Vojíř and Jiří Matas and Matej Kristan},
journal= {arXiv preprint arXiv:1711.09594},
year = {2019}
}