Abstract: The disclosure provides systems and methods for automatically and noninvasively analyzing livestock health, wherein to determine at least one of a body composition indicator or a pose indicator based on the data acquired from the camera; store the body composition indicator or pose indicator in a data record associated with the animal of interest; and provide the body composition indicator or pose indicator to a neural network trained to predict an animal outcome for animals of a similar species to the animal of interest.

Abstract: This disclosure describes various attributes and implementations of systems and methods for efficiently generating high accuracy landmark annotations for depth-based image or video data sets. For example, a Transpositional Tagging approach can automatically or semi-automatically find, identify, and track landmarks (such as human or animal joints, other structural landmarks, or other points of interest) that are visible in one imaging modality (such as infrared, optical, etc.), and transfer those labels to a second image modality (such as, e.g., near-IR depth images/videos). As a result, systems and methods provided herein can quickly generate highly specific training datasets that are then used to develop neural network models for detecting poses, positions, and/or movements in imaging modalities such as 3D and depth images.