  The field of visual object tracking is dominated by methods that combine
simple tracking algorithms and ad hoc schemes. Probabilistic tracking
algorithms, which are leading in other fields, are surprisingly absent from the
leaderboards. We found that accounting for distance in target kinematics,
exploiting detector confidence and modelling non-uniform clutter
characteristics is critical for a probabilistic tracker to work in visual
tracking. Previous probabilistic methods fail to address most or all these
aspects, which we believe is why they fall so far behind current
state-of-the-art (SOTA) methods (there are no probabilistic trackers in the
MOT17 top 100). To rekindle progress among probabilistic approaches, we propose
a set of pragmatic models addressing these challenges, and demonstrate how they
can be incorporated into a probabilistic framework. We present BASE (Bayesian
Approximation Single-hypothesis Estimator), a simple, performant and easily
extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and
MOT20, without using Re-Id. Code will be made available at
https://github.com/ffi-no
