The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
ATRW: A Benchmark for Amur Tiger Re-identification in the Wild
Open accessAbstract
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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“for example, accuracy exceeded 90% in a study of 92 tigers across 8000 video clips103.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [103]
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Research works printed but not linked: 1
- 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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Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
Liu, X., Min, W., Mei, S., Wang, L., & Jiang, S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049334 · Cited by 201
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
Kreiss, S., Bertoni, L., & Alahi, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3124981 · Cited by 184
Part-Guided Attention Learning for Vehicle Instance Retrieval
Zhang, X., Zhang, R., Cao, J., Gong, D., You, M., & Shen, C. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3030301 · Cited by 116
Perspectives on Individual Animal Identification from Biology and Computer Vision
Vidal, M., Wolf, N., Rosenberg, B., Harris, B. P., & Mathis, A. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab107 · Cited by 104
Metadata
- 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
- FWCI
- 4.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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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