  Tracking-by-detection methods have demonstrated competitive performance in
recent years. In these approaches, the tracking model heavily relies on the
quality of the training set. Due to the limited amount of labeled training
data, additional samples need to be extracted and labeled by the tracker
itself. This often leads to the inclusion of corrupted training samples, due to
occlusions, misalignments and other perturbations. Existing
tracking-by-detection methods either ignore this problem, or employ a separate
component for managing the training set.
  We propose a novel generic approach for alleviating the problem of corrupted
training samples in tracking-by-detection frameworks. Our approach dynamically
manages the training set by estimating the quality of the samples. Contrary to
existing approaches, we propose a unified formulation by minimizing a single
loss over both the target appearance model and the sample quality weights. The
joint formulation enables corrupted samples to be down-weighted while
increasing the impact of correct ones. Experiments are performed on three
benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color
with 128 videos. On the OTB-2015, our unified formulation significantly
improves the baseline, with a gain of 3.8% in mean overlap precision. Finally,
our method achieves state-of-the-art results on all three datasets. Code and
supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .
