Abstract
Ensemble discriminative tracking utilizes a committee of
classifiers, to label data samples, which are in turn, used for
retraining the tracker to localize the target using the collective knowledge of the committee. Committee members could
vary in their features, memory update schemes, or training
data, however, it is inevitable to have committee members
that excessively agree because of large overlaps in their
version space. To remove this redundancy and have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another,
covering the version space with minimum overlaps. In this
study, we propose an online ensemble tracker that directly
generates a diverse committee by generating an efficient set
of artificial training. The artificial data is sampled from the
empirical distribution of the samples taken from both target and background, whereas the process is governed by
query-by-committee to shrink the overlap between classi-
fiers. The experimental results demonstrate that the proposed scheme outperforms conventional ensemble trackers
on public benchmarks