  Deep learning methods for computer vision tasks show promise for automating
the data analysis of camera trap images. Ecological camera traps are a common
approach for monitoring an ecosystem's animal population, as they provide
continual insight into an environment without being intrusive. However, the
analysis of camera trap images is expensive, labour intensive, and time
consuming. Recent advances in the field of deep learning for object detection
show promise towards automating the analysis of camera trap images. Here, we
demonstrate their capabilities by training and comparing two deep learning
object detection classifiers, Faster R-CNN and YOLO v2.0, to identify,
quantify, and localize animal species within camera trap images using the
Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data
sets. When trained on large labeled datasets, object recognition methods have
shown success. We demonstrate their use, in the context of realistically sized
ecological data sets, by testing if object detection methods are applicable for
ecological research scenarios when utilizing transfer learning. Faster R-CNN
outperformed YOLO v2.0 with average accuracies of 93.0\% and 76.7\% on the two
data sets, respectively. Our findings show promising steps towards the
automation of the labourious task of labeling camera trap images, which can be
used to improve our understanding of the population dynamics of ecosystems
across the planet.
