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

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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

DOI 10.1145/3394171.3413569

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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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Liang Zheng Liyue Shen Lu Tian Shengjin Wang Jingdong Wang and Qi Tian. 2015b. Scalable Person Re-identification: A Benchmark. In ICCV. Liang Zheng Liyue Shen Lu Tian Shengjin Wang Jingdong Wang and Qi Tian. 2015b. Scalable Person Re-identification: A Benchmark. In ICCV. · DOI

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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)
ORCID: Li, Shuyuan
Li, Jianguo
Tang, Hanlin
Qian, Rui
Lin, Weiyao
Venue: Proceedings of the 28th ACM International Conference on Multimedia
Publisher: ACM
Published: 2020-10-12
Pages: 2590-2598
DOI: 10.1145/3394171.3413569
OpenAlex: W3093326439
Cited by: 121
References: 45
Citations by year: 2026: 15 · 2025: 20 · 2024: 27 · 2023: 26 · 2022: 21 · 2021: 9 · 2020: 2
FWCI: 4.47
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
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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ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

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