The Mouse Action Recognition System (MARS): pose annotation data

The study of naturalistic social behavior requires quantification of animals' interactions. This is generally done through manual annotation—a highly time consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We recently introduced the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice (Segalin et al, 2020). This Dataset includes the training, test, and validation sets used to train MARS's supervised classifiers for three social behaviors of interest: close investigation, mounting, and attack. Included in the dataset are 15000 pairs of top- and front-view frames from videos of pairs of interacting mice. Each frame has been manually annotated by five individuals for a total of nine (top-view) or thirteen (front-view) keypoints: the nose, ears, base of neck, hips, base of tail, middle of tail, and end of tail, and additionally the four paws (front-view only.)