  Monitoring the population and movements of endangered species is an important
task to wildlife conversation. Traditional tagging methods do not scale to
large populations, while applying computer vision methods to camera sensor data
requires re-identification (re-ID) algorithms to obtain accurate counts and
moving trajectory of wildlife. However, existing re-ID methods are largely
targeted at persons and cars, which have limited pose variations and
constrained capture environments. This paper tries to fill the gap by
introducing a novel large-scale dataset, the Amur Tiger Re-identification in
the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur
tigers, with bounding box, pose keypoint, and tiger identity annotations. In
contrast to typical re-ID datasets, the tigers are captured in a diverse set of
unconstrained poses and lighting conditions. We demonstrate with a set of
baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we
propose a novel method for tiger re-identification, which introduces precise
pose parts modeling in deep neural networks to handle large pose variation of
tigers, and reaches notable performance improvement over existing re-ID
methods. The dataset is public available at https://cvwc2019.github.io/ .
