Tracking animals without markers in the wild

Researchers from the Cluster of Excellence Collective Behaviour 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.

Two pigeons are pecking grains in a park in Konstanz. A third pigeon flies in. There are four cameras in the immediate vicinity. Doctoral students Alex Chan and Urs Waldmann from the Cluster of Excellence Collective Behaviour at the University of Konstanz are filming the scene. After an hour, they return with the footage to their office to analyze it with a computer vision framework for posture estimation and identity tracking. The framework detects and draws a box around all pigeons.

3D-MuPPET showcases a powerful and flexible framework for researchers who would like to use 3D posture reconstruction for multiple individuals to study collective behaviour in any environments or species. As long as a multi-camera setup and a 2D posture estimator is available, the framework can be applied to track 3D postures of any animals.Urs Waldmann, Alex Hoi Hang Chan, Hemal Naik, Máté Nagy, Iain D. Couzin, Oliver Deussen, Bastian Goldluecke, Fumihiro Kano.

A new field promises to usher in a new era of using machine learning and computer vision to tackle small and large-scale questions about the biology of organisms around the ... A team of researchers has developed novel approaches using computer vision and deep learning to resolve the problem of low-level vision in videos caused by rain and night-time conditions, as well as ...FOSSILS & RUINS

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