Researchers developed a computer vision framework for posture estimation and identity tracking which they can use in indoor environments as well as in the wild. They have thus taken an important step towards markerless tracking of animals in the wild using computer vision and machine learning.
Researchers from the IBS Center, KAIST, and the Institute for Basic Science collaborated to develop a new analytical tool known as Spectrogram-UMAP-Based Temporal-Link Embedding (SUBTLE), which uses artificial intelligence to learn how to classify and analyze animal behavior based on 3D movement data.
Researchers from the Cluster of Excellence Collective Behavior have developed a computer vision framework for posture estimation and identity tracking that they can use in indoor environments as well as in the wild. This is an important step toward the markerless tracking of animals in the wild using computer vision and machine learning.
Animal behavior analysis is a fundamental tool in various studies, ranging from basic neuroscience research to understanding the causes and treatments of diseases. It is widely applied not only in biological research but also across various industrial fields, including robotics.
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