Automated Behavioral Analysis Using Instance Segmentation
Chen Yang, Jeremy Forest, Matthew Einhorn, Thomas A. Cleland

TL;DR
This paper introduces a transfer learning approach using instance segmentation for animal behavior analysis, enabling effective tracking and analysis with limited labeled data, and provides an open-source tool for the research community.
Contribution
It presents a novel transfer learning method leveraging instance segmentation for animal behavior analysis, requiring minimal labeled data and offering an open-source implementation.
Findings
Achieves performance comparable to humans in behavior analysis tasks.
Requires only a small number of labeled images for training.
Provides an open-source tool for the research community.
Abstract
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…
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Taxonomy
TopicsAnimal Behavior and Welfare Studies · Bat Biology and Ecology Studies
