Dear All,
we invite you to a DPZ Colloquium on Tuesday, September 10th, at 4 p.m. (c.t.):
Alexander Mathis (Harvard)
DeepLabCut: a deep learning tool for fast, robust, and efficient 3D pose estimation of multiple individuals
Quantifying behavior is crucial for many applications across the life sciences and engineering. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming and computationally challenging. I will present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. I will demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors from egg-laying fruits flies to hunting cheetahs. Furthermore, I will highlight recent work on tracking multiple animals during social and collective behaviors as well as ways to score their behavior automatically.
The talk will be in the DPZ Old lecture hall.
With best regards
Christian Schloegl
