Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts.

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

SUBMITTER: Torney CJ 

PROVIDER: S-EPMC4881999 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature