no code implementations • 20 Sep 2016 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
Compared to the standard exhaustive scale search, our approach achieves a gain of 2. 5% in average overlap precision on the OTB dataset.
no code implementations • CVPR 2016 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks.
no code implementations • ICCV 2015 • Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg
These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood.