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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 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.