  Photographs of wild animals in their natural habitats can be recorded
unobtrusively via cameras that are triggered by motion nearby. The installation
of such camera traps is becoming increasingly common across the world. Although
this is a convenient source of invaluable data for biologists, ecologists and
conservationists, the arduous task of poring through potentially millions of
pictures each season introduces prohibitive costs and frustrating delays. We
develop automatic algorithms that are able to detect animals, identify the
species of animals and to recognize individual animals for two species. we
propose the first fully-automatic tool that can recognize specific individuals
of leopard and tiger due to their characteristic body markings. We adopt a
class of supervised learning approach of machine learning where a Deep
Convolutional Neural Network (DCNN) is trained using several instances of
manually-labelled images for each of the three classification tasks. We
demonstrate the effectiveness of our approach on a data set of camera-trap
images recorded in the jungles of Southern India.
