no code implementations • 25 Oct 2021 • Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W. Mathis, Frank van Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D. Couzin, Grant van Horn, Margaret C. Crofoot, Charles V. Stewart, Tanya Berger-Wolf
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging.
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data.
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem.
Neural networks are highly effective tools for pose estimation.
Ranked #1 on Animal Pose Estimation on Horse-10
Quantifying behavior is crucial for many applications in neuroscience.