Learning Spatial Distribution of Long-Term Trackers Scores
Vincenzo Mariano Scarrica Antonino Staiano
Long-Term tracking is a hot topic in Computer Vision. In this context,
competitive models are presented every year, showing a constant growth rate in
performances, mainly measured in standardized protocols as Visual Object
Tracking (VOT) and Object Tracking Benchmark (OTB). Fusion-trackers strategy
has been applied over last few years for overcoming the known re-detection
problem, turning out to be an important breakthrough. Following this approach,
this work aims to generalize the fusion concept to an arbitrary number of
trackers used as baseline trackers in the pipeline, leveraging a learning phase
to better understand how outcomes correlate with each other, even when no
target is present. A model and data independence conjecture will be evidenced
in the manuscript, yielding a recall of 0.738 on LTB-50 dataset when learning
from VOT-LT2022, and 0.619 by reversing the two datasets. In both cases,
results are strongly competitive with state-of-the-art and recall turns out to
be the first on the podium.