  Visual tracking is fundamentally the problem of regressing the state of the
target in each video frame. While significant progress has been achieved,
trackers are still prone to failures and inaccuracies. It is therefore crucial
to represent the uncertainty in the target estimation. Although current
prominent paradigms rely on estimating a state-dependent confidence score, this
value lacks a clear probabilistic interpretation, complicating its use.
  In this work, we therefore propose a probabilistic regression formulation and
apply it to tracking. Our network predicts the conditional probability density
of the target state given an input image. Crucially, our formulation is capable
of modeling label noise stemming from inaccurate annotations and ambiguities in
the task. The regression network is trained by minimizing the Kullback-Leibler
divergence. When applied for tracking, our formulation not only allows a
probabilistic representation of the output, but also substantially improves the
performance. Our tracker sets a new state-of-the-art on six datasets, achieving
59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are
available at https://github.com/visionml/pytracking.
