FuCoLoT -- A Fully-Correlational Long-Term Tracker
This work addresses the challenge of robust long-term tracking for applications like UAV surveillance, representing a significant advance over existing methods.
The paper tackles the problem of long-term visual object tracking by proposing FuCoLoT, a fully-correlational tracker that uses a novel detector for efficient target re-detection and failure estimation, achieving state-of-the-art results with a 19% improvement on the UAV20L benchmark and running at 15fps with reduced memory usage.
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.