  Camera traps are a proven tool in biology and specifically biodiversity
research. However, camera traps including depth estimation are not widely
deployed, despite providing valuable context about the scene and facilitating
the automation of previously laborious manual ecological methods. In this
study, we propose an automated camera trap-based approach to detect and
identify animals using depth estimation. To detect and identify individual
animals, we propose a novel method D-Mask R-CNN for the so-called instance
segmentation which is a deep learning-based technique to detect and delineate
each distinct object of interest appearing in an image or a video clip. An
experimental evaluation shows the benefit of the additional depth estimation in
terms of improved average precision scores of the animal detection compared to
the standard approach that relies just on the image information. This novel
approach was also evaluated in terms of a proof-of-concept in a zoo scenario
using an RGB-D camera trap.
