  We propose an improved discriminative model prediction method for robust
long-term tracking based on a pre-trained short-term tracker. The baseline
pre-trained short-term tracker is SuperDiMP which combines the bounding-box
regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP
improves SuperDiMP in the following three aspects: (1) Uncertainty reduction
using random erasing: To make our model robust, we exploit an agreement from
multiple images after erasing random small rectangular areas as a certainty.
And then, we correct the tracking state of our model accordingly. (2) Random
search with spatio-temporal constraints: we propose a robust random search
method with a score penalty applied to prevent the problem of sudden detection
at a distance. (3) Background augmentation for more discriminative feature
learning: We augment various backgrounds that are not included in the search
area to train a more robust model in the background clutter. In experiments on
the VOT-LT2020 benchmark dataset, the proposed method achieves comparable
performance to the state-of-the-art long-term trackers. The source code is
available at: https://github.com/bismex/RLT-DIMP.
