An Animal Detection Pipeline for Identification
Abstract
This paper proposes a 5-component detection pipeline for use in a computer vision-based animal recognition system. The end result of our proposed pipeline is a collection of novel annotations of interest (AoI) with species and view-point labels. These AoIs, for example, could be fed as the focused input data into an appearance-based animal identification system. The goal of our method is to increase the reliability and automation of animal censusing studies and to provide better ecological information to conservationists. Our method is able to achieve a localization mAP of 81.67%, a species and viewpoint annotation classification accuracy of 94.28% and 87.11%, respectively, and an AoI accuracy of 72.75% across 6 animal species of interest. We also introduce the Wildlife Image and Localization Dataset (WILD), which contains 5,784 images and 12,007 labeled annotations across 28 classification species and a variety of challenging, real-world detection scenarios.
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Wildbook image analysis can start with images from any source and use ML and computer vision to detect multiple animals, classify their species, and identify individual animals.
“The image analysis of Wildbook can start with images from any source—scientists, camera traps, drones, community scientists, or social media—and use ML and computer vision to detect multiple animals in the images100 to not only classify their species, but identify individual animals applying a suite of different algorithms101,147.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [100]
Computer vision recently emerged as a powerful alternative for automatic individual identification.
“Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification54,98–100.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [100]
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Self-citationBalakrishna, K., & Dhanushree, V. (2023) · International Conference on Recent Trends in Electronics and Communication (ICRTEC) · doi:10.1109/icrtec56977.2023.10111888 · Cited by 4 · see citing works
Retracted: Deep learning insights and methods for classifying wildlife
Thalor, M. A., Nagabhyrava, R., Rajkumar, K., Chakraborty, A., Singh, R., & Aswal, U. S. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10183057 · Cited by 4 · see citing works
RetractedBakana, S. R., Zhang, Y., & Twala, B. (2020) · International Conference on Image Processing and Machine Vision (IPMV) · doi:10.1145/3421558.3421584 · Cited by 3 · see citing works
Kumar, R. J. R., & Stanley, B. F. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202447203015 · Cited by 3 · see citing works
Intelligent Image-Based Defense Against Incursion from The Wild Animals
Ajaykarthik, R. T., Arunbabu, R., K.Aditya, Dineshkumar, G., & E.pugazhenthi (2024) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi:10.47392/irjaeh.2024.0053 · Cited by 3 · see citing works
Underwater Visible Light Communication for Scuba Divers Health Monitoring System
Ajaykarthik, R. T., Kamalesh.N, В, Н. К., & Yogeshwaran.B (2024) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi:10.47392/irjaeh.2024.0051 · Cited by 3 · see citing works
Price, C. S., McClanahan, A. L., Burge, E. J., Houppermans, T., & Holmberg, J. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1362703 · Cited by 3 · see citing works
Self-citationAnimal Detection and Recognition in Day Light Videos Using Deep Learning
Sudharshan Duth, P., Dhanyashree, A. N., & Chandana, P. (2024) · International Conference on Networking, Embedded and Wireless Systems (ICNEWS) · doi:10.1109/icnews60873.2024.10730894 · Cited by 3 · see citing works
The Animal ID Problem: Continual CurationPreprint
Stewart, C. V., Parham, J. R., Holmberg, J., & Berger-Wolf, T. Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10377 · Cited by 3 · see citing works
Self-citationDynaDog+T: A Parametric Animal Model for Synthetic Canine Image GenerationPreprint
Deane, J., Kearney, S., Kim, K. I., & Cosker, D. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07330 · Cited by 8 · see citing works
Transfer Learning Based Wildlife Recognition for Tele-Observation in Field Occlusion Environment
Wang, H., Song, Y., Li, S., Dai, W., & Liu, J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803691 · Cited by 2 · see citing works
Animal Intrusion Detection System using SIFT Features and Transfer Learning with MobileNetV2
Chandralekha, E., Muzammil Ali, A., Ritesh, V., & Srinivasan, M. K. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426584 · Cited by 2 · see citing works
Identification of Animals Through Footprint Analysis
Dharmik, R. C., Bhardwaj, A., Bisen, L., Bhisikar, G., Baghele, C., & Nayak, H. (2024) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa64000.2024.10882320 · Cited by 2 · see citing works
Tombak, K. J., Nonnamaker, L., Parham, J. R., Stewart, C. V., Warungu, R., & Rubenstein, D. I. (2025) · Animal Behaviour · doi:10.1016/j.anbehav.2025.123158 · Cited by 2 · see citing works
Self-citationAnimal reidentification using restricted set classification
Kuncheva, L. I. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101225 · Cited by 2 · see citing works
Rodent Tracking and Abnormal Behavior Classification in Live Video using Deep Neural Networks
Gnanasekar, S. T., Yanushkevich, S. N., van den Hoogen, N. J., & Trang, T. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022203 · Cited by 2 · see citing works
Blount, D., Holmberg, J., Parham, J. R., Gero, S., Gordon, J. C. D., & Levenson, J. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.22.473895 · Cited by 2 · see citing works
Self-citationPavitra, A. R. R., Kathiroli, P., Rajalakshmi, D., & Chinnasamy, A. (2024) · International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) · doi:10.1109/adics58448.2024.10533193 · Cited by 1 · see citing works
CNN‐Based Flank Predictor for Quadruped Animal Species
Suessle, V., Heurich, M., Downs, C. T., Weinmann, A., & Hergenröther, E. (2025) · IET Computer Vision · doi:10.1049/cvi2.70024 · Cited by 1 · see citing works
KAMERA: Enhancing Aerial Surveys of Ice-Associated Seals in Arctic Environments
Romlein, A., Hou, B. X., Boss, Y., Christman, C. L., Koslovsky, S. M., Moreland, E. E., Parham, J., & Hoogs, A. J. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00228 · Cited by 1 · see citing works
Self-citationSpotted correctly? Human expertise remains essential in AI ‐assisted identification of Eurasian lynx
Blašković, S., Sindičić, M., Reilly, M., Topličanec, I., & Gomerčić, T. (2025) · Journal of Zoology · doi:10.1111/jzo.70095 · Cited by 1 · see citing works
Creating Evaluation Function of Two Camera Positions for Feature-rich Deer Photography
Yamazaki, K., & Katsuma, R. (2022) · IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) · doi:10.1109/apwimob56856.2022.10014273 · Cited by 1 · see citing works
Highway Collision Avoidance by Detection of Animal’s Images
Mahima, R., Meenu, M., Manjari, K., Rovenal, S., Keerthana Sri, S., & Sruthi Meera, P. (2023) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot56793.2023.10053391 · Cited by 1 · see citing works
Wild Animal Recognition Using an Edge Device
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A Case Study: Remote Animal Monitoring
Velasco-Montero, D., Fernández‐Berni, J., & Rodriguez-Vazquez, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-90903-1_6
Detecting Wildlife Trapped Images Using Automatically Shared Nearest Neighbouring Pixels (ASNNP)
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The application of big data to the conservation of wildlife in highland areas
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IoT-enabled deep learning framework for accurate animal head tracking
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Lan, Z., Huang, Y., Peng, L., Chen, M., Liao, S., Lu, Z., & An, S. (2025) · Internet Technology Letters · doi:10.1002/itl2.70176
AI-Based Wildlife Intrusion Detection Using Daytime, Thermal, and Predictive Analysis
Candace, Y. L., Naidu, M. M., Deepika, R. K., & Jayashree, K. (2026) · International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) · doi:10.1109/icsmile69273.2026.11519189
AgroShield: AI-Enabled Animal Detection & Alert System
Balaji Prabhu, B., V, P, S. G., C, A. B., L, K. M., Muskan, & G, P. B. (2026) · International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) · doi:10.1109/ecmi68341.2026.11603163
- Authors
- Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- Kitware (United States) (US)
- Princeton University (US)
- University of Illinois Chicago (US)
- ORCID
- Venue
- 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2018-03
- Pages
- 1075-1083
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2802043043
- Cited by
- 103
- References
- 22
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 20 · 2023: 17 · 2022: 24 · 2021: 10 · 2020: 15 · 2019: 4
- FWCI
- 2.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- animal detection, animal identification, wildlife census, species annotation, animal localization, conservation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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