Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for Nocturnal Activity Tracking.

Autor: Lund, Silje Marquardsen, Nielsen, Jonas, Gammelgård, Frej, Nielsen, Maria Gytkjær, Jensen, Trine Hammer, Pertoldi, Cino
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Zdroj: Animals (2076-2615); Oct2024, Vol. 14 Issue 19, p2820, 21p
Abstrakt: Simple Summary: This paper presents a way to automate computer vision processes applied to behavior recognition on closed-circuit television (CCTV) footage of two captive African elephants. Object detection software using both Create ML and DeepLabCut was used to control the accuracy of using such models, and those models were subsequently used to analyze seven days' worth of nighttime footage to assess the general behavioral patterns of the elephants, showcasing the possibility of using automated tools for behavioral analysis. 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 by comparison with manually scoring, before being applied to seven nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data were 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 behavioral 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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