Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Open accessAbstract
Significance: Motion-sensor cameras in natural habitats offer the opportunity to inexpensively and unobtrusively gather vast amounts of data on animals in the wild. A key obstacle to harnessing their potential is the great cost of having humans analyze each image. Here, we demonstrate that a cutting-edge type of artificial intelligence called deep neural networks can automatically extract such invaluable information. For example, we show deep learning can automate animal identification for 99.3% of the 3.2 million-image Snapshot Serengeti dataset while performing at the same 96.6% accuracy of crowdsourced teams of human volunteers. Automatically, accurately, and inexpensively collecting such data could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences.
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Computer vision can be used to classify species from camera traps, supporting a real-time, less labor-intensive species count.
“For instance, camera traps take photos automatically whenever a motion sensor is activated—computer vision can be used to classify the species that pass by, supporting a real-time, less labor-intensive species count [64,585,643].”
From Tackling climate change with machine learning (2023) · cites this paper as [585]
Detecting and classifying animal species with DL for population estimates is becoming increasingly popular for images, acoustic spectrograms, and videos.
“For these reasons, detecting and classifying animal species with DL for the purpose of population estimates is becoming increasingly popular for images52,53, acoustic spectrograms95, and videos96.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [53]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,649 · see citing works
(2016) · Cited by 11,679 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443 · see citing works
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2015) · doi:10.1109/cvpr31182.2015 · Cited by 3,735 · see citing works
A Bayesian Hierarchical Model for Learning Natural Scene Categories
Fei-Fei, L., & Perona, P. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.16 · Cited by 3,657 · see citing works
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works
Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206757 · Cited by 2,895 · see citing works
International Conference on Machine Learning (ICML)-2005
津田, 宏. (2006) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 2,558 · see citing works
Tsoumakas, G., & Katakis, I. (2007) · International Journal of Data Warehousing and Mining · doi:10.4018/jdwm.2007070101 · Cited by 2,512 · see citing works
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
Classifier chains for multi-label classification
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011) · Machine Learning · doi:10.1007/s10994-011-5256-5 · Cited by 2,312 · see citing works
Deep Convolutional Neural Networks for Hyperspectral Image Classification
Hu, W., Huang, Y., Wei, L., Zhang, F., & Li, H.-C. (2015) · Journal of Sensors · doi:10.1155/2015/258619 · Cited by 1,962 · see citing works
2013 IEEE 10th International Symposium on Biomedical Imaging
(2013) · doi:10.1109/isbi20048.2013 · Cited by 1,637 · see citing works
Some studies in machine learning using the game of checkers
Samuel, A. L. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0206 · Cited by 1,439 · see citing works
Bridle, J. S. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_28 · Cited by 1,278 · see citing works
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,222 · see citing works
Settles, B. (2012) · Cited by 1,183 · see citing works
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298684 · Cited by 1,155 · see citing works
End-to-end attention-based large vocabulary speech recognition
Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472618 · Cited by 1,129 · see citing works
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122 · see citing works
Caruana, R. (1998) · Cited by 1,110 · see citing works
Foundations of Machine Learning
(2012) · The MIT Press eBooks · doi:10.7551/mitpress/8291.003.0006 · Cited by 1,102 · see citing works
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Müller, A., Sejdinović, D., & Piechocki, R. J. (2012) · Cited by 1,001 · see citing works
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Sener, O., & Savarese, S. (2017) · arXiv · doi:10.48550/arxiv.1708.00489 · Cited by 922 · see citing works
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Dauphin, Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2572 · Cited by 827 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799 · see citing works
Camera Traps in Animal Ecology
O’Connell, A. F., Karanth, K. U., & Nichols, J. D. (2010) · Springer eBooks · doi:10.1007/978-4-431-99495-4 · Cited by 768 · see citing works
2015 IEEE International Conference on Image Processing (ICIP)
(2015) · doi:10.1109/icip30522.2015 · Cited by 726 · see citing works
Towards Perspective-Free Object Counting with Deep Learning
Oñoro-Rubio, D., & López-Sastre, R. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_38 · Cited by 650 · see citing works
Camera trap, line transect census and track surveys: a comparative evaluation
Silveira, L., Jácomo, A. T. A., & Diniz‐Filho, J. A. F. (2003) · Biological Conservation · doi:10.1016/s0006-3207(03)00063-6 · Cited by 643 · see citing works
Camera traps in animal ecology : methods and analyses
O’Connell, A. F., Nichols, J. D., & Kāranta, K. U. (2011) · Cited by 615 · see citing works
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605 · see citing works
Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577 · see citing works
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015) · Scientific Data · doi:10.1038/sdata.2015.26 · Cited by 510 · see citing works
Self-citationAdvances in Neural Information Processing Systems 26 (NIPS 2013)
Cseke, B., Opper, M., & Sanguinetti, G. (2013) · Cited by 405 · see citing works
Wang, H., Cruz-Roa, Á., Basavanhally, A. N., Gilmore, H. L., Shih, N. N. C., Feldman, M., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2014) · Journal of medical imaging · doi:10.1117/1.jmi.1.3.034003 · Cited by 349 · see citing works
Lin, M., Chen, Q., & Yan, S. (2013) · arXiv · doi:10.48550/arxiv.1312.4400 · Cited by 252 · see citing works
Swanson, A., Kosmala, M., Lintott, C., & Packer, C. (2016) · Conservation Biology · doi:10.1111/cobi.12695 · Cited by 251 · see citing works
Self-citationAutomated identification of animal species in camera trap images
Yu, X., Wang, J., Kays, R., Jansen, P. A., Wang, T., & Huang, T. S. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-52 · Cited by 232 · see citing works
Deep convolutional neural network based species recognition for wild animal monitoring
Chen, G., Han, T. X., He, Z., Kays, R., & Forrester, T. D. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025172 · Cited by 190 · see citing works
Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M., & Packer, C. (2017) · Ecology Letters · doi:10.1111/ele.12832 · Cited by 172 · see citing works
Self-citationCounting Everyday Objects in Everyday Scenes
Chattopadhyay, P., Vedantam, R., Selvaraju, R. R., Batra, D., & Parikh, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.471 · Cited by 160 · see citing works
Bowkett, A. E., Rovero, F., & Marshall, A. R. (2007) · African Journal of Ecology · doi:10.1111/j.1365-2028.2007.00881.x · Cited by 136 · see citing works
In the absence of a “landscape of fear”: How lions, hyenas, and cheetahs coexist
Swanson, A., Arnold, T. W., Kosmala, M., Forester, J. D., & Packer, C. (2016) · Ecology and Evolution · doi:10.1002/ece3.2569 · Cited by 115 · see citing works
Self-citationAnderson, T. M., White, S., Davis, B., Erhardt, R., Palmer, M. S., Swanson, A., Kosmala, M., & Packer, C. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0314 · Cited by 111 · see citing works
Self-citationAutomatic Storage and Analysis of Camera Trap Data
Harris, G. M., Thompson, R. W., Childs, J. L., & Sanderson, J. G. (2010) · Bulletin of the Ecological Society of America · doi:10.1890/0012-9623-91.3.352 · Cited by 110 · see citing works
Data acquisition and management software for camera trap data: A case study from the TEAM Network
Fegraus, E., Lin, K., Ahumada, J., Baru, C., Chandra, S., & Youn, C. (2011) · Ecological Informatics · doi:10.1016/j.ecoinf.2011.06.003 · Cited by 100 · see citing works
A Novel Method to Reduce Time Investment When Processing Videos from Camera Trap Studies
Swinnen, K. R., Reijniers, J., Breno, M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098881 · Cited by 89 · see citing works
De-multiplexing vortex modes in optical communications using transport-based pattern recognition
Park, S. R., Cattell, L., Nichols, J. M., et al. (2018) · Optics Express · doi:10.1364/oe.26.004004 · Cited by 75 · see citing works
Yang, L., Wang, B., Qi, H., et al. (2015) · Journal of Sensors · doi:10.1155/2015/504869 · Cited by 60 · see citing works
Kashif, M. N., Raza, S. E. A., Sirinukunwattana, K., Arif, M., & Rajpoot, N. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493441 · Cited by 56 · see citing works
Software for minimalistic data management in large camera trap studies
Krishnappa, Y. S., & Turner, W. C. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2014.06.004 · Cited by 47 · see citing works
Gómez, A., Díez, G., Salazar, A., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50835-1_67 · Cited by 42 · see citing works
Feature Design for Offline Arabic Handwriting Recognition: Handcrafted vs Automated?
Chherawala, Y., Roy, P. P., & Cheriet, M. (2013) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.65 · Cited by 39 · see citing works
Fast Automatic Detection of Wildlife in Images from Trap Cameras
Figueroa, K., Camarena–Ibarrola, A., García, J. R. L., & Tejeda, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12568-8_114 · Cited by 31 · see citing works
Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases
Rampášek, L., & Goldenberg, A. (2018) · Cell · doi:10.1016/j.cell.2018.02.013 · Cited by 29 · see citing works
Gómez, A., Salazar, A., & Vargas, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06169 · Cited by 17 · see citing works
Palmer, M. S., & Packer, C. (2018) · African Journal of Ecology · doi:10.1111/aje.12505 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [62]Ren S He K Girshick R Sun J 2015 Faster R-CNN: Towards real-time object detection with region proposal networks 2015 Advances in Neural Information Processing Systems (NIPS) Neural Information Processing Systems Foundation La Jolla, CA
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The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294 · see citing works
Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119 · see citing works
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 · see citing works
Applications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724 · see citing works
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679 · see citing works
Machine learning to classify animal species in camera trap images: Applications in ecology
Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Di Salvo, P. A., Lewis, J. S., White, M. D., et al. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13120 · Cited by 504 · see citing works
Self-citationA review of Earth Artificial Intelligence
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271 · see citing works
Shahinfar, S., Meek, P., & Falzon, G. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101085 · Cited by 229 · see citing works
Chimpanzee face recognition from videos in the wild using deep learning
Schofield, D., Nagrani, A., Zisserman, A., Hayashi, M., Matsuzawa, T., Biro, D., & Carvalho, S. (2019) · Science Advances · doi:10.1126/sciadv.aaw0736 · Cited by 224 · see citing works
Deep learning‐based methods for individual recognition in small birds
Ferreira, A. C., Silva, L. R., Renna, F., Brandl, H. B., Renoult, J. P., Farine, D. R., Covas, R., & Doutrelant, C. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13436 · Cited by 203 · see citing works
Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., Jadeyegowda, M., & Kiesecker, J. M. (2022) · Sustainability · doi:10.3390/su14127154 · Cited by 194 · see citing works
The Rise of Artificial Intelligence under the Lens of Sustainability
Khakurel, J., Penzenstadler, B., Porras, J., Knutas, A., & Zhang, W. (2018) · Technologies · doi:10.3390/technologies6040100 · Cited by 187 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Beery, S., Wu, G., Rathod, V., Votel, R., & Huang, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01309 · Cited by 140 · see citing works
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Palmer, M. S., Huebner, S. E., Willi, M., Fortson, L., & Packer, C. (2021) · Human Computation · doi:10.15346/hc.v8i2.123 · Cited by 20 · see citing works
Self-citation
- Authors
- Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J.
- Affiliations
- University of Wyoming (US)
- Auburn University (US)
- Harvard University (US)
- University of Oxford (GB)
- University of Minnesota (US)
- Corresponding author
- Clune, Jeff
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2018-06-05
- Volume
- 115
- Issue
- 25
- Pages
- E5716-E5725
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6016780
- OpenAlex
- W2769210209
- Cited by
- 1,230
- References
- 77
- Citations by year
- 2026: 170 · 2025: 155 · 2024: 147 · 2023: 177 · 2022: 157 · 2021: 174 · 2020: 136 · 2019: 83 · 2018: 29 · 2017: 2
- FWCI
- 52.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Auburn University Scholarly Repository
- Funders
- National Science Foundation (1453549)
- 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
- camera traps, animal species identification, animal counting, animal behavior classification, convolutional neural networks, automated data extraction, wildlife conservation
- MeSH terms
- Machine Learning, Algorithms, Animals, Animals, Wild, Artificial Intelligence, Behavior, Animal, Ecology, Humans, Neural Networks, Computer, Ecosystem
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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