Pose‐Guided Re‐Identification of Amur Tigers Under Wild Environmental Constraints

ABSTRACT The conservation of endangered species is contingent upon accurate and efficient wildlife monitoring, which is essential for informed decision‐making and effective preservation strategies. With the global population of Amur tigers (Panthera tigris altaica) falling below 600, innovative conservation strategies are critically needed. Traditional monitoring methods have fallen short in accuracy and efficiency, leading to a shift towards leveraging big data and artificial intelligence for effective wildlife surveillance. Existing re‐identification techniques struggle with natural habitat challenges like occlusions, changing poses, varying light, and limited data. To overcome these issues, we propose the pose‐guided dual branch re‐identification network (PDBRNet). Our approach integrates pose estimation to guide feature disentanglement and alignment, crucial for accurate re‐identification, while an image preprocessing method considering illumination factors mitigates lighting variations' impact on accuracy. Through validation on the occluded and illumination‐varying amur tiger (OIAT) dataset, PDBRNet demonstrates exceptional performance. Specifically, in single‐camera scenarios, PDBRNet achieves an outstanding mean average precision (mAP) of 79.4, surpassing the performance of PGCFL (51.6) and PPGNet (69.7). Moreover, in cross‐camera scenarios, PDBRNet maintains its superiority with a remarkable mAP of 54.0, along with Rank‐1 and Rank‐5 scores of 97.8 and 98.9, respectively, showcasing its robustness in real‐world surveillance applications. The introduction of PDBRNet significantly enhances re‐identification accuracy and holds promise for addressing complexities in field environments, contributing significantly to wildlife conservation efforts.