Video-based analysis of animal behaviour#
Overview#
This course introduces free, open-source tools for tracking animal motion from videos and extracting quantitative descriptions of behaviour from motion tracks.
We’ll be going through the following chapters of the ‘Animals in Motion’ handbook:
Chapter 3: Pose Estimation with SLEAP. Hands-on practice with SLEAP to train a pose estimation model, evaluate its performance, and use it to predict poses on new video frames.
Chanpter 4: Analysing tracks with movement. Hands on practice with movement to load predicted pose tracks in Python, clean them, compute kinematic variables, and visualise the results.
Note
Training and prediction with pose estimation models are GPU-intensive tasks. Since many students will not have access to a GPU on their own machine, we highly recommend that they also attend the follow-up course on Running pose estimation on the SWC HPC system. This will cover how to run pose estimation at scale, using the GPUs of the SWC HPC cluster.
Instructors#
Prerequisites#
Please carefully read through the prerequisites page of the ‘Animals in Motion’ handbook, listing hardware, software, and data requirements. Make sure to set aside half an hour ahead of the course to install the necessary software and download the sample data.
If you encounter any installation issues, please contact Niko Sirmpilatze.
Note
If you are an incoming PhD student attending the full General Software Skills for Systems Neuroscience course you will have likely already installed general development tools so you may skip that section of the prerequisites.