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 15Â fps in a single CPU thread.
CITATION STYLE
Lukežič, A., Zajc, L. Č., Vojíř, T., Matas, J., & Kristan, M. (2019). FuCoLoT – A Fully-Correlational Long-Term Tracker. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11362 LNCS, pp. 595–611). Springer Verlag. https://doi.org/10.1007/978-3-030-20890-5_38
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