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The ability of a researcher to re-identify (re-ID) an individual animal uponre-encounter is fundamental for addressing a broad range of questions in thestudy of ecosystem function, community and population dynamics, and behaviouralecology. In this review, we describe a brief history of camera traps for re-ID,present a collection of computer vision feature engineering methodologiespreviously used for animal re-ID, provide an introduction to the underlyingmechanisms of deep learning relevant to animal re-ID, highlight the success ofdeep learning methods for human re-ID, describe the few ecological studiescurrently utilizing deep learning for camera trap analyses, and our predictionsfor near future methodologies based on the rapid development of deep learningmethods. By utilizing novel deep learning methods for object detection andsimilarity comparisons, ecologists can extract animals from an image/video dataand train deep learning classifiers to re-ID animal individuals beyond thecapabilities of a human observer. This methodology will allow ecologists withcamera/video trap data to re-identify individuals that exit and re-enter thecamera frame. Our expectation is that this is just the beginning of a majortrend that could stand to revolutionize the analysis of camera trap data and,ultimately, our approach to animal ecology.

Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data