Version 1
: Received: 27 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (09:13:23 CEST)
How to cite:
Lund, S. M.; Nielsen, J.; Gammelgård, F.; Nielsen, M. G.; Jensen, T. H.; Pertoldi, C. Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking.. Preprints2024, 2024082008. https://doi.org/10.20944/preprints202408.2008.v1
Lund, S. M.; Nielsen, J.; Gammelgård, F.; Nielsen, M. G.; Jensen, T. H.; Pertoldi, C. Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking.. Preprints 2024, 2024082008. https://doi.org/10.20944/preprints202408.2008.v1
Lund, S. M.; Nielsen, J.; Gammelgård, F.; Nielsen, M. G.; Jensen, T. H.; Pertoldi, C. Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking.. Preprints2024, 2024082008. https://doi.org/10.20944/preprints202408.2008.v1
APA Style
Lund, S. M., Nielsen, J., Gammelgård, F., Nielsen, M. G., Jensen, T. H., & Pertoldi, C. (2024). Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking.. Preprints. https://doi.org/10.20944/preprints202408.2008.v1
Chicago/Turabian Style
Lund, S. M., Trine Hammer Jensen and Cino Pertoldi. 2024 "Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for nocturnal activity tracking." Preprints. https://doi.org/10.20944/preprints202408.2008.v1
Abstract
This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
