Fig. 1

Schematic workflow. Data processing starts with the acquisition of synchronized, multi-view videos, which serve as input to the SfM reconstruction pipeline to recover camera positions and movement. In addition, Mask R-CNN predictions, after training the detection model on a subset of images, result in segmented masks for each video frame, from which animal poses can be estimated. These serve as locations of multi-view animals trajectories in the pixel coordinate system. Subsequently, trajectories can be triangulated using known camera parameters and positions from the SfM pipeline, yielding 3D animal trajectories and poses. Integrating the environmental information from the scene reconstruction, these data can be used for in depth downstream analyses