An Automated AI Framework for Quantitative Measurement of Mammalian Behavior
Jia Liu , Tao Liu , Zhengfeng Hu , Fan Wu , Wenjie Guo , Haojie Wu , Zhan Wang , Yiyi Men , Shuang Yin , Paul A. Garber , Derek Dunn , Colin A. Chapman , Gang He , Felix Guo , Ruliang Pan , Tongzuo Zhang , Yang Zhao , Pengfei Xu , Baoguo Li , Songtao Guo
Integrative Zoology ›› 2026, Vol. 21 ›› Issue (2) : 343 -358.
Despite the large amount of video data captured during ethological studies of wild mammals, there is no widely accepted method available to automatically and quantitatively measure and analyze animal behavior. We developed a framework using facial recognition and deep learning to automatically track, measure, and quantify the behavior of single or multiple individuals from 10 distinct mammalian taxa, including three species of primates, three species of bovids, three species of carnivores, and one species of equid. We used spatiotemporal information based on animal skeleton models to recognize a set of distinct behaviors such as walking, feeding, grooming, and resting, and achieved an accuracy ranging from 0.82 to 0.96. Accuracies of validation videos ranged from 0.80 to 0.99. Our study offers an innovative analytical platform for the rapid and accurate evaluation of animal behavior in both captive and field settings.
animal behavior / behavior recognition and measurement / deep learning
2025 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.
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