FuCoLoT–a fully-correlational long-term tracker

Alan Lukezic, Luka Cehovin, Tomas Vojir, Jiri Matas, Matej Kristan

Abstract:

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.