  The ongoing biodiversity crisis calls for accurate estimation of animal
density and abundance to identify sources of biodiversity decline and
effectiveness of conservation interventions. Camera traps together with
abundance estimation methods are often employed for this purpose. The necessary
distances between camera and observed animals are traditionally derived in a
laborious, fully manual or semi-automatic process. Both approaches require
reference image material, which is both difficult to acquire and not available
for existing datasets. We propose a fully automatic approach we call AUtomated
DIstance esTimation (AUDIT) to estimate camera-to-animal distances. We leverage
existing state-of-the-art relative monocular depth estimation and combine it
with a novel alignment procedure to estimate metric distances. AUDIT is fully
automated and requires neither the comparison of observations in camera trap
imagery with reference images nor capturing of reference image material at all.
AUDIT therefore relieves biologists and ecologists from a significant workload.
We evaluate AUDIT on a zoo scenario dataset unseen during training where we
achieve a mean absolute distance estimation error over all animal instances of
only 0.9864 meters and mean relative error (REL) of 0.113. The code and usage
instructions are available at
https://github.com/PJ-cs/DistanceEstimationTracking
