  The ability of a researcher to re-identify (re-ID) an individual animal upon
re-encounter is fundamental for addressing a broad range of questions in the
study of ecosystem function, community and population dynamics, and behavioural
ecology. In this review, we describe a brief history of camera traps for re-ID,
present a collection of computer vision feature engineering methodologies
previously used for animal re-ID, provide an introduction to the underlying
mechanisms of deep learning relevant to animal re-ID, highlight the success of
deep learning methods for human re-ID, describe the few ecological studies
currently utilizing deep learning for camera trap analyses, and our predictions
for near future methodologies based on the rapid development of deep learning
methods. By utilizing novel deep learning methods for object detection and
similarity comparisons, ecologists can extract animals from an image/video data
and train deep learning classifiers to re-ID animal individuals beyond the
capabilities of a human observer. This methodology will allow ecologists with
camera/video trap data to re-identify individuals that exit and re-enter the
camera frame. Our expectation is that this is just the beginning of a major
trend that could stand to revolutionize the analysis of camera trap data and,
ultimately, our approach to animal ecology.
