Researchers at EPFL have made strides in computer-aided animal tracking by expanding their software, DeepLabCut, to offer high-performance tracking of multiple animals in videos. The ability to capture the behavior of animals is critical for neuroscience, ecology, and many other fields. Cameras are ideal for capturing fine-grained behavior, but developing computer vision techniques to extract the animal's behavior is challenging even though this seems effortless for our own visual system. One of the key aspects of quantifying animal behavior is -pose estimation-, which refers to the ability of a computer to identify the pose (position and orientation of different body parts) of an animal. In a lab setting, it-s possible to assist pose estimation by placing markers on the animal's body like in motion-capture techniques used in movies (think Gollum in the Lord of the Rings). But as one can imagine, getting animals to wear specialized equipment is not the easiest task, and downright impossible and unethical in the wild. For this reason, Professors Alexander Mathis and Mackenzie Mathis at EPFL have been pioneering -markerless- tracking for animals.
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