Abstract
AbstractUnderstandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.
Funder
National Natural Science Foundation of China
Guoqiang Institute of Tsinghua University
Strategic Priority Research Program of the Chinese Academy of Sciences
It is also supported by Tsinghua-Peking Joint Center for Life Sciences, the Thousand-Talent Young Investigator Program, the IDG/McGovern Institute for Brain Research.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference70 articles.
1. Houpt K. A. Domestic animal behavior for veterinarians and animal scientists, 6th edn. John Wiley & Sons, Inc., (2018).
2. Reimert, I., Bolhuis, J. E., Kemp, B. & Rodenburg, T. B. Indicators of positive and negative emotions and emotional contagion in pigs. Physiol. Behav. 109, 42–50 (2013).
3. Camerlink, I. & Ursinus, W. W. Tail postures and tail motion in pigs: a review. Appl. Anim. Behav. Sci. 230, 105079 (2020).
4. Matthews, S. G., Miller, A. L., Clapp, J., Plotz, T. & Kyriazakis, I. Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J. 217, 43–51 (2016).
5. Wang, S. L. et al. The research progress of vision-based artificial intelligence in smart pig farming. Sens.-Basel 22, 6541 (2022).
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