  Quantifying behavior is crucial for many applications in neuroscience.
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. In motor control studies,
humans or other animals are often marked with reflective markers to assist with
computer-based tracking, yet markers are intrusive (especially for smaller
animals), and the number and location of the markers must be determined a
priori. Here, we present a highly efficient method for markerless tracking
based on transfer learning with deep neural networks that achieves excellent
results with minimal training data. We demonstrate the versatility of this
framework by tracking various body parts in a broad collection of experimental
settings: mice odor trail-tracking, egg-laying behavior in drosophila, and
mouse hand articulation in a skilled forelimb task. For example, during the
skilled reaching behavior, individual joints can be automatically tracked (and
a confidence score is reported). Remarkably, even when a small number of frames
are labeled ($\approx 200$), the algorithm achieves excellent tracking
performance on test frames that is comparable to human accuracy.
