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Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts

Author(s): Torney, Colin J.; Dobson, Andrew P.; Borner, Felix; Lloyd-Jones, David J.; Moyer, David; et al

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dc.contributor.authorTorney, Colin J.-
dc.contributor.authorDobson, Andrew P.-
dc.contributor.authorBorner, Felix-
dc.contributor.authorLloyd-Jones, David J.-
dc.contributor.authorMoyer, David-
dc.contributor.authorMaliti, Honori T.-
dc.contributor.authorMwita, Machoke-
dc.contributor.authorFredrick, Howard-
dc.contributor.authorBorner, Markus-
dc.contributor.authorHopcraft, J. Grant C.-
dc.date.accessioned2019-05-30T15:54:59Z-
dc.date.available2019-05-30T15:54:59Z-
dc.date.issued2016-05-26en_US
dc.identifier.citationTorney, Colin J, Dobson, Andrew P, Borner, Felix, Lloyd-Jones, David J, Moyer, David, Maliti, Honori T, Mwita, Machoke, Fredrick, Howard, Borner, Markus, Hopcraft, J Grant C. (2016). Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts. PLOS ONE, 11 (5), e0156342 - e0156342. doi:10.1371/journal.pone.0156342en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1313c-
dc.description.abstractAccurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.en_US
dc.format.extente0156342 - e0156342en_US
dc.language.isoen_USen_US
dc.relationhttps://github.com/ctorney/wildCounten_US
dc.relation.ispartofPLOS ONEen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleAssessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Countsen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1371/journal.pone.0156342-
dc.date.eissued2016-05-26en_US
dc.identifier.eissn1932-6203-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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