Automated Behavioral Analysis Using Instance Segmentation
Chen Yang Jeremy Forest Matthew Einhorn Thomas A. Cleland
Animal behavior analysis plays a crucial role in various fields, such as life
science and biomedical research. However, the scarcity of available data and
the high cost associated with obtaining a large number of labeled datasets pose
significant challenges. In this research, we propose a novel approach that
leverages instance segmentation-based transfer learning to address these
issues. By capitalizing on fine-tuning the classification head of the instance
segmentation network, we enable the tracking of multiple animals and facilitate
behavior analysis in laboratory-recorded videos. To demonstrate the
effectiveness of our method, we conducted a series of experiments, revealing
that our approach achieves exceptional performance levels, comparable to human
capabilities, across a diverse range of animal behavior analysis tasks.
Moreover, we emphasize the practicality of our solution, as it requires only a
small number of labeled images for training. To facilitate the adoption and
further development of our method, we have developed an open-source
implementation named Annolid (An annotation and instance segmentation-based
multiple animal tracking and behavior analysis package). The codebase is
publicly available on GitHub at https://github.com/cplab/annolid. This resource
serves as a valuable asset for researchers and practitioners interested in
advancing animal behavior analysis through state-of-the-art techniques.