Cited by 128Video Surveillance and Tracking MethodsHuman Pose and Action RecognitionWildlife Ecology and Conservation

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

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Li, Shuyuan, Li, Jianguo, Tang, Hanlin, Qian, Rui, Lin, Weiyao·2020·Proceedings of the 28th ACM International Conference on Multimedia

Abstract

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 ATRW dataset is public available at https://cvwc2019.github.io/challenge.html

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Authors
Li, S., Li, J., Tang, H., Qian, R., & Lin, W.
Affiliations
  • Shanghai Jiao Tong University (CN)
  • Ant Group (China) (CN)
  • Intel (United States) (US)
Venue
Proceedings of the 28th ACM International Conference on Multimedia
Publisher
ACM
Published
2020-10-12
Pages
2590-2598
OpenAlex
W3093326439
Cited by
121
References
45
Citations by year
2026: 15 · 2025: 20 · 2024: 27 · 2023: 26 · 2022: 21 · 2021: 9 · 2020: 2 · 2019: 1
FWCI
4.47
Type
conference paper
Language
en
Open access
Locations
  • ACM International Conference on Multimedia (ACM MM) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Keywords
Amur tiger, large pose variation, endangered species monitoring

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