An Animal Detection Pipeline for Identification

Jason Parham, Charles Stewart, Jonathan Crall, Daniel Ian Rubenstein, Jason Holmberg, Tanya Berger-Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

This paper proposes a 5-component detection pipeline for use in a computer vision-based animal recognition system. The end result of our proposed pipeline is a collection of novel annotations of interest (AoI) with species and view-point labels. These AoIs, for example, could be fed as the focused input data into an appearance-based animal identification system. The goal of our method is to increase the reliability and automation of animal censusing studies and to provide better ecological information to conservationists. Our method is able to achieve a localization mAP of 81.67%, a species and viewpoint annotation classification accuracy of 94.28% and 87.11%, respectively, and an AoI accuracy of 72.75% across 6 animal species of interest. We also introduce the Wildlife Image and Localization Dataset (WILD), which contains 5,784 images and 12,007 labeled annotations across 28 classification species and a variety of challenging, real-world detection scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1075-1083
Number of pages9
ISBN (Electronic)9781538648865
DOIs
StatePublished - May 3 2018
Event18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States
Duration: Mar 12 2018Mar 15 2018

Publication series

NameProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Volume2018-January

Other

Other18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
CountryUnited States
CityLake Tahoe
Period3/12/183/15/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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