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Unsupervised Quantification of Animal Behavior

This repository is a collection of different work-in-progress approaches in unsupervised machine learning for the quantification of animal behavior.

The main analysis pipeline does or will soon contain:

  • markerless pose estimation from video data with DeepLabCut
  • multi-view triangulation and 3D reconstruction with Anipose
  • UMAP dimensionality reduction
  • stochastic modeling of time series data with Hidden Markov Models (HMM)
  • behavioral clustering with Variational Embeddings of Animal Motion (VAME), Toeplitz Inverse Covariance-Based Clustering (TICC) and others