  Non intrusive monitoring of animals in the wild is possible using camera
trapping framework, which uses cameras triggered by sensors to take a burst of
images of animals in their habitat. However camera trapping framework produces
a high volume of data (in the order on thousands or millions of images), which
must be analyzed by a human expert. In this work, a method for animal species
identification in the wild using very deep convolutional neural networks is
presented. Multiple versions of the Snapshot Serengeti dataset were used in
order to probe the ability of the method to cope with different challenges that
camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and
98.1% in Top-5 in the evaluation set using a residual network topology. Also,
the results show that the proposed method outperforms previous approximations
and proves that recognition in camera-trap images can be automated.
