  Imagine you are a dog behind a fence $Q$ and a hiker is passing by at
constant speed along the hiking path $P$. In order to fulfil your duties as a
watchdog, you desire to bark as long as possible at the human. However, your
barks can only be heard in a fixed radius $\rho$ and, as a dog, you have
bounded speed $s$. Can you optimize your route along the fence $Q$ in order to
maximize the barking time with radius $\rho$, assuming you can run backwards
and forward at speed at most $s$?
  We define the barking distance from a polyline $P$ on $n$ vertices to a
polyline $Q$ on $m$ vertices as the time that the hiker stays in your barking
radius if you run optimally along $Q$. This asymmetric similarity measure
between two curves can be used to detect outliers in $Q$ compared to $P$ that
other established measures like the Fr\'echet distance and Dynamic Time Warping
fail to capture at times. We consider this measure in three different settings.
In the discrete setting, the traversals of $P$ and $Q$ are both discrete. For
this case we show that the barking distance from $P$ to $Q$ can be computed in
$O(nm\log s)$ time. In the semi-discrete setting, the traversal of $Q$ is
continuous while the one of $P$ is again discrete. Here, we show how to compute
the barking distance in time $O(nm\log (nm))$. Finally, in the continuous
setting in which both traversals are continuous, we show that the problem can
be solved in polynomial time. For all the settings we show that, assuming SETH,
no truly subquadratic algorithm can exist.
