| Autor: |
Yaning Han, Ke Chen, Yunke Wang, Wenhao Liu, Xiaojing Wang, Jiahui Liao, Yiting Huang, Chuanliang Han, Kang Huang, Jiajia Zhang, Shengyuan Cai, Zhouwei Wang, Yongji Wu, Gao Gao, Nan Wang, Jinxiu Li, Yangwangzi Song, Jing Li, Guodong Wang, Liping Wang, Yaping Zhang, Pengfei Wei |
| Rok vydání: |
2023 |
| DOI: |
10.1101/2023.03.05.531235 |
| Popis: |
The study of social behaviors in animals is essential for understanding their survival and reproductive strategies. However, accurately tracking and analyzing the social interactions of free-moving animals has remained a challenge. Existing multi-animal pose estimation techniques suffer from drawbacks such as the need for extensive manual annotation and difficulty in discriminating between similar-looking animals in close social interactions. In this paper, we present the Social Behavior Atlas (SBeA), a novel computational framework that solves these challenges by employing a deep learning-based video instance segmentation model, 3D pose reconstruction, and unsupervised dynamic behavioral clustering. SBeA framework also involves a multi-camera setup to prevent occlusion, and a novel approach to identify individual animals in close social interactions. We demonstrate the effectiveness of SBeA in tracking and mapping the 3D close interactions of free-moving animals using the example of genetic mutant mice, birds, and dogs. Our results show that SBeA is capable of identifying subtle social interaction abnormalities, and the models and frameworks developed can be applied to a wide range of animal species. SBeA is a powerful tool for researchers in the fields of neuroscience and ecology to study animal social behaviors with a high degree of accuracy and reliability. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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