BOVIDS: A deep learning-based software for pose estimation to evaluate
nightly behavior and its application to Common Elands (Tragelaphus oryx)
in zoos
Abstract
Only a few studies on the nocturnal behavior of African ungulates exist
so far, with mostly small sample sizes. For a comprehensive
understanding of nocturnal behavior, this database needs to be expanded.
Zoo animals offer a good opportunity to lay the corresponding
foundations. The results can provide clues for the study of wild animals
and furthermore contribute to a better understanding of animal welfare
and better husbandry conditions in zoos. To tackle this open question,
we developed a stand-alone open-source software based on deep learning
techniques, named BOVIDS (Behavioral Observations by Videos and Images
using a Deep-Learning Software). This software is used to identify
ungulates in their enclosure and to determine crucial behavioral poses
on video material with an accuracy of 99.4%. A case study on 25 Common
Elands (Tragelaphus oryx) out of 5 EAZA zoos with a total of 11,411
hours video material out of 822 nights is conducted, yielding the first
detailed description of the nightly behavior of Common Elands. Our
results indicate that age and sex are influencing factors on the
nocturnal activity budget, the length of behavioral phases as well as
the number of phases per behavioral state during the night. Finally, the
results suggest the existence of species-specific rhythms that open
future research directions.