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ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification

Kulits, Peter and Wall, Jake and Bedetti, Anka and Henley, Michelle and Beery, Sara (2021) ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification. In: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). Association for Computing Machinery , New York, NY, pp. 88-98. ISBN 978-1-4503-8453-7.

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African elephants are vital to their ecosystems, but their populations are threatened by a rise in human-elephant conflict and poaching. Monitoring population dynamics is essential in conservation efforts; however, tracking elephants is a difficult task, usually relying on the invasive and sometimes dangerous placement of GPS collars. Although there have been many recent successes in the use of computer vision techniques for automated identification of other species, identification of elephants is extremely difficult and typically requires expertise as well as familiarity with elephants in the population. We have built and deployed a web-based platform and database for human-in-the-loop re-identification of elephants combining manual attribute labeling and state-of-the-art computer vision algorithms, known as ElephantBook. Our system is currently in use at the Mara Elephant Project, helping monitor the protected and at-risk population of elephants in the Greater Maasai Mara ecosystem. ElephantBook makes elephant re-identification usable by non-experts and scalable for use by multiple conservation NGOs.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Henley, Michelle0000-0002-1675-7388
Beery, Sara0000-0002-2544-1844
Additional Information:© 2021 Copyright held by the owner/author(s). We would like to thank the entire team at the Mara Elephant Project for their efforts in deploying this system. This work was supported, through funding, data storage, and computing resources, by Microsoft AI for Earth, the Caltech Resnick Sustainability Institute, and NSF GRFP Grant No. 1745301, the views are those of the authors and do not necessarily reflect the views of these organizations.
Group:Resnick Sustainability Institute
Funding AgencyGrant Number
Microsoft AI for EarthUNSPECIFIED
Resnick Sustainability InstituteUNSPECIFIED
NSF Graduate Research FellowshipDGE-1745301
Subject Keywords:elephants, re-identification, conservation tech
Record Number:CaltechAUTHORS:20211117-175445588
Persistent URL:
Official Citation:Peter Kulits, Jake Wall, Anka Bedetti, Michelle Henley, and Sara Beery. 2021. ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification. In ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (COMPASS ’21), June 28-July 2, 2021, Virtual Event, Australia. ACM, New York, NY, USA, 11 pages.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111923
Deposited By: Tony Diaz
Deposited On:17 Nov 2021 23:27
Last Modified:17 Nov 2021 23:27

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