Work

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

Year: 2025

Type: article

Abstract: 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.

Source: IET Image Processing

DOI: 10.1049/ipr2.70160

Authors: Tianyu Wang, Boxuan Ma, Xinrui Zhao, Chao Mou, Jiahua Fan

Institutions: Beijing Forestry University, State Forestry and Grassland Administration

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,740

Funders: National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities