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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Accuracy exceeded 90% in a study of 92 tigers across 8000 video clips.
“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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Lazarus, B. A., Che-Amat, A., Shah, M. M. A. H., Hamdan, A., Hassim, H. A., Mustaffa-Kamal, F., Azizan, T. R. P. T., Noor, M. H. M., Mustapha, N. M., & Ahmad, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-89980-0 · Cited by 12 · see citing works
Animal Re-Identification Algorithm for Posture Diversity
He, Z., Qian, J., Yan, D., Wang, C., & Xin, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094783 · Cited by 12 · see citing works
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NORPPA: NOvel Ringed Seal Re-Identification by Pelage Pattern Aggregation
Nepovinnykh, E., Eerola, T., Kalviainen, H. A., & Chelak, I. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00008 · Cited by 12 · see citing works
Deep Learning-based Approaches for Preventing and Predicting Wild Animals Disappearance: A Review
Djibrine, O. H., Ahmat, D., & Boukar, M. M. (2024) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa59508.2024.10467213 · Cited by 11 · see citing works
Cross-Domain Animal Pose Estimation With Skeleton Anomaly-Aware Learning
Han, L., Chen, K., Zhao, L., Jiang, Y., Wang, P., & Zheng, N. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3557844 · Cited by 10 · see citing works
Adaptive High-Frequency Transformer for Diverse Wildlife Re-identification
Li, C., Chen, S., & Ye, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72784-9_17 · Cited by 9 · see citing works
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Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data
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Fast and Efficient Model for Real-Time Tiger Detection In The Wild
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Giant panda age recognition based on a facial image deep learning system
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Re-identification of Saimaa Ringed Seals from Image Sequences
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DepthFormer: A High-Resolution Depth-Wise Transformer for Animal Pose Estimation
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KITPose: Keypoint-Interactive Transformer for Animal Pose Estimation
Rao, J., Xu, T., Song, X., Feng, Z., & Wu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18907-4_51 · Cited by 8 · see citing works
A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification
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MAPoseNet: Animal pose estimation network via multi-scale convolutional attention
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Self-citationESCAPE: Encoding Super-keypoints for Category-Agnostic Pose Estimation
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MMVSL: A multi-modal visual semantic learning method for pig pose and action recognition
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Amur Tiger Individual Identification Based on the Improved InceptionResNetV2
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SCAPE: A Simple and Strong Category-Agnostic Pose Estimator
Liang, Y., Ye, Z., Liu, W., & Lü, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_27 · Cited by 6 · see citing works
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Animal Pose Estimation Based on 3D Priors
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Self-citationAnimalRTPose: Faster cross-species real-time animal pose estimation
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Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm
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Re-Identification of Individual Kākā: An Explainable DINO-Based Model
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2D Animal Skeletons Keypoint Detection: Research Progress and Future Trends
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Recurrence over Video Frames (RoVF) for Animal Re-identification
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Transformer-based Models for Enhanced Amur Tiger Re-Identification
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Retracted: Deep learning insights and methods for classifying wildlife
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RetractedAn Efficient Illumination Invariant Tiger Detection Framework for Wildlife Surveillance
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CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx
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Automatic Identification of Individual African Leopards in Unlabeled Camera Trap Images
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The Hitchhiker's Guide to Endangered Species Pose Estimation
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Identity-Preserving Animal Image Generation for Animal Individual Identification
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Incremental Learning for Animal Pose Estimation using RBF k-DPP
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A Novel Approach To Lion Re-Identification Using Vision Transformers
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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
- 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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