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. and Hopcraft, J. G. C. (2016) Assessing rotation-invariant feature classification for automated wildebeest population counts. PLoS ONE, 11(5), e0156342. (doi: 10.1371/journal.pone.0156342) (PMID:27227888) (PMCID:PMC4881999)

[img]
Preview
Text
120726.PDF - Published Version
Available under License Creative Commons Attribution.

998kB

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.

Item Type:Articles
Additional Information:CJT is supported by a Complex Systems Scholar Award from the James S. McDonnell Foundation. JGCH is supported by a Lord Kelvin Adam Smith Fellowship, funding from the British Ecological Society and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641918 AfricanBioServices.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Borner, Dr Markus and Torney, Dr Colin and Hopcraft, Dr Grant
Authors: Torney, C. J., Dobson, A. P., Borner, F., Lloyd-Jones, D. J., Moyer, D., Maliti, H. T., Mwita, M., Fredrick, H., Borner, M., and Hopcraft, J. G. C.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN:1932-6203
ISSN (Online):1932-6203
Published Online:26 May 2016
Copyright Holders:Copyright © 2016 Torney et al.
First Published:First published in PLoS ONE 11(5):e0156342
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record