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Monitoring the population and movements of endangered species is an importanttask to wildlife conversation. Traditional tagging methods do not scale tolarge populations, while applying computer vision methods to camera sensor datarequires re-identification (re-ID) algorithms to obtain accurate counts andmoving trajectory of wildlife. However, existing re-ID methods are largelytargeted at persons and cars, which have limited pose variations andconstrained capture environments. This paper tries to fill the gap byintroducing a novel large-scale dataset, the Amur Tiger Re-identification inthe Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amurtigers, with bounding box, pose keypoint, and tiger identity annotations. Incontrast to typical re-ID datasets, the tigers are captured in a diverse set ofunconstrained poses and lighting conditions. We demonstrate with a set ofbaseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, wepropose a novel method for tiger re-identification, which introduces precisepose parts modeling in deep neural networks to handle large pose variation oftigers, and reaches notable performance improvement over existing re-IDmethods. The dataset is public available at https://cvwc2019.github.io/ .

ATRW: A Benchmark for Amur Tiger Re-identification in the Wild