The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A computer vision for animal ecology
Open accessAbstract
A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The “majority illusion” in social networks
Lerman, K., Yan, X., & Wu, X.-Z. (2016) · PLOS ONE · doi:10.1371/journal.pone.0147617 · Cited by 669
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015) · Scientific Data · doi:10.1038/sdata.2015.26 · Cited by 510
Automated image-based tracking and its application in ecology
Dell, A. I., Bender, J. A., Branson, K., Couzin, I. D., de Polavieja, G. G., Noldus, L. P. J. J., Pérez-Escudero, A., Perona, P., Straw, A. D., Wikelski, M., et al. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.05.004 · Cited by 492
ARZOUMANIAN, Z., HOLMBERG, J., & NORMAN, B. (2005) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2005.01117.x · Cited by 316
Assessing rotation-invariant feature classification for automated wildebeest population counts
Torney, C. J., Dobson, A. P., Borner, F., Lloyd-Jones, D. J., Moyer, D., Maliti, H. T., Mwita, M., Fredrick, H., Borner, M., & Hopcraft, J. G. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156342 · Cited by 32
Research works printed but not linked: 90
- Abràmoff M. D., 2004, Image processing with imageJ, Biophotonics International, 11, 36
- 10.1578/AM.32.3.2006.374 · doi:10.1578/am.32.3.2006.374
- 10.1016/j.patcog.2007.11.010 · DOI
- 10.1016/j.patrec.2015.08.015 · DOI
- 10.1007/s00300-007-0317-8 · doi:10.1007/s00300-007-0317-8
- 10.1371/journal.pone.0158748 · DOI
- Bartomeus I., 2016, A common framework for identifying rules across different types of interactions, Functional Ecology, 10, 1
- 10.1578/AM.31.2.2005.243 · doi:10.1578/am.31.2.2005.243
- 10.1038/srep23166 · DOI
- 10.1016/j.patrec.2015.11.023 · DOI
- Berg T., 2014, Birdsnap: Large‐scale fine‐grained visual categorization of birds, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019
- 10.1109/JPROC.2009.2032355 · DOI
- Blanc K., 2014, Fish species recognition from video using SVM classifier, Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data – MAED ‘14, 1
- 10.1111/j.2041-210X.2012.00212.x · DOI · Paper page
- Bowley C., 2017, Proceedings of the 2016 IEEE 12th International Conference on e‐Science, e‐Science 2016, 251
- Bradski G., 2000, The OpenCV Library, Dr Dobbs Journal, 25, 120
- S. Branson C. Wah F. Schroff B. Babenko P. Welinder P. Perona S. Belongie K. Daniilidis P. Maragos N. Paragios 2010 Springer Berlin Germany
- 10.1111/jofo.12171 · DOI · Paper page
- 10.1109/ICIP.2014.7025172 · DOI · Paper page
- 10.3390/s16111904 · DOI
- 10.1109/WACV.2013.6475023 · DOI · Paper page
- Cross M. D., 2014, Pattern‐recognition software as a supplemental method of identifying individual eastern box turtles (Terrapene c. carolina), Herpetological Review, 45, 584
- 10.1139/juvs-2016-0008 · doi:10.1139/juvs-2016-0008
- 10.1590/1982-0224-20150074 · DOI
- 10.1080/00063657.2011.588195 · DOI
- 10.1109/eScience.2013.50 · DOI
- 10.1016/j.patcog.2014.07.017 · DOI
- 10.1525/bio.2013.63.11.8 · doi:10.1525/bio.2013.63.11.8
- 10.1111/syen.12146 · DOI
- 10.1016/j.patcog.2015.09.012 · DOI
- 10.1371/journal.pone.0088655 · DOI
- Gilman A., 2016, 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ), 1
- Giraldo‐Zuluaga J.‐H. Gomez A. Salazar A. &Diaz‐Pulido A.(2017).Camera‐trap images segmentation using multi‐layer robust principal component analysis. arXiv:1701.08180 · DOI
- Gomez A., 2016, Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12–14, 2016, Proceedings, Part I, 747, 10.1007/978-3-319-50835-1_67 · DOI
- 10.1080/01431161.2010.489068 · DOI
- 10.1109/SMC.2016.7844347 · doi:10.1109/smc.2016.7844347
- 10.7717/peerj.563 · DOI
- 10.1371/journal.pone.0079556 · DOI · Paper page
- Howland J. C., 2012, OCEANS 2012 MTS/IEEE: Harnessing the Power of the Ocean
- 10.1016/j.ecoinf.2013.07.006 · DOI · Paper page
- 10.1007/s00338-008-0354-y · doi:10.1007/s00338-008-0354-y
- 10.3390/rs8090726 · DOI
- 10.1016/j.tree.2013.02.013 · DOI · Paper page
- 10.1093/bioinformatics/btp699 · DOI · Paper page
- 10.1111/cobi.12809 · DOI
- 10.1002/wsb.596 · doi:10.1002/wsb.596
- 10.1111/2041-210X.12331 · doi:10.1111/2041-210x.12331
- 10.1242/bio.20149175 · doi:10.1242/bio.20149175
- 10.1016/j.ecoinf.2015.10.008 · DOI
- 10.1007/s00300-011-1138-3 · DOI
- 10.1899/09-080.1 · DOI
- Marburg A., 2016, Deep learning for benthic fauna identification, 1
- 10.1016/j.aasri.2014.09.006 · DOI
- 10.1371/journal.pone.0166773 · DOI
- 10.3389/fpls.2016.01419 · DOI · Paper page
- 10.1007/s00338-008-0459-3 · doi:10.1007/s00338-008-0459-3
- 10.1111/2041-210X.12326 · DOI · Paper page
- 10.1111/2041-210X.12036 · DOI
- 10.1016/j.tree.2015.08.008 · DOI · Paper page
- 10.1016/j.ecoinf.2016.11.003 · DOI
- 10.1016/j.neucom.2015.10.122 · DOI · Paper page
- 10.1186/1687-5281-2013-48 · doi:10.1186/1687-5281-2013-48
- Ren X., 2013, Ensemble video object cut in highly dynamic scenes, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1947
- 10.1242/jeb.142281 · DOI
- 10.1038/srep45127 · DOI · Paper page
- 10.1016/j.cviu.2013.12.005 · DOI · Paper page
- Stauffer C., 1999, Adaptive background mixture models for real‐time tracking, 246
- 10.1071/MF13139 · doi:10.1071/mf13139
- 10.1038/ncomms5117 · DOI · Paper page
- 10.1038/srep32059 · DOI
- Sun X. Shi J. Dong J. &Wang X.(2017).Fish recognition from low‐resolution underwater images. InProceedings of the 9th International Congress on Image and Signal Processing BioMedical Engineering and Informatics CISP‐BMEI 2016 pp.471–476. · DOI
- 10.1111/cobi.12695 · DOI · Paper page
- 10.1098/rstb.2008.0211 · doi:10.1098/rstb.2008.0211
- 10.1002/ece3.587 · DOI
- 10.1186/s12862-016-0854-2 · DOI
- 10.1002/ajp.22446 · DOI
- Van Horn G., 2015, Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine‐grained dataset collection, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 595
- Van Horn G. Mac Aodha O. Song Y. Shepard A. Adam H. Perona P. &Belongie S.(2017).The iNaturalist Challenge 2017 Dataset. arXiv preprint arXiv:1707.06642. · arXiv
- Villon S., 2016, Advanced Concepts for Intelligent Vision Systems. ACIVS 2016
- 10.1111/2041-210X.12320 · DOI
- Weinstein B. G., 2017, Data from: A computer vision for animal ecology, Dryad Digital Repository
- 10.1111/ele.12730 · DOI
- 10.1109/WACV.2013.6475020 · DOI
- 10.3390/rs8050375 · DOI
- 10.1111/bij.12690 · doi:10.1111/bij.12690
- 10.1371/journal.pone.0102942 · DOI
- 10.1186/1687-5281-2013-52 · DOI · Paper page
- 10.1186/1687-5281-2013-46 · DOI
- 10.1109/TMM.2016.2594138 · DOI
- Zhang H., 2012, Mining Photo‐sharing Websites to Study Ecological Phenomena, 749
Cited by
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
Background subtraction in real applications: Challenges, current models and future directions
Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
“To better understand and monitor wildlife biodiversity loss, ecologists commonly deploy camera traps— heat or motion-activated static cameras placed in the wild (Wearn & Glover-Kapfer, 2017)—and then use ML models to process the data collected (Weinstein, 2018; Norouzzadeh et al., 2019; Tabak et al., 2019; Beery et al…”
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231
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
“However, relying on 86 humans for individual identification and data collection is extremely time-consuming 87 (Weinstein, 2018).”
Background
Metadata
- Authors
- Weinstein, B. G.
- Venue
- Journal of Animal Ecology
- Publisher
- Wiley
- Published
- 2017-11-29
- Volume
- 87
- Issue
- 3
- Pages
- 533-545
- ISSN
- 0021-8790 (ISSN-L)
- 1365-2656
- OpenAlex
- W2767556927
- Cited by
- 429
- References
- 6
- FWCI
- 17.63
- Type
- journal article
- Language
- en
- Open access
- bronze
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A computer vision for animal ecology. https://app.cochat.ai/papers/10.1111/1365-2656.12780
- The paper
- Weinstein, B. G. (2018). A computer vision for animal ecology. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.12780
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.