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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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Abstract: Accurate 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.
Publication Date: 26-May-2016
Electronic Publication Date: 26-May-2016
Citation: Torney, 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.0156342
DOI: doi:10.1371/journal.pone.0156342
EISSN: 1932-6203
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Pages: e0156342 - e0156342
Type of Material: Journal Article
Journal/Proceeding Title: PLOS ONE
Version: Final published version. This is an open access article.

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