Abstract: The disclosure provides a computer implemented method for detecting an output pose of interest of a subject in real-time, the method comprising recording an image frame of the subject using an imaging device, determining an output pose of interest by processing the image frame using a machine learning model that comprises a rule-based pose inference model and a data-driven pose inference model wherein with the data-driven pose inference model, determining a data-driven pose of interest by processing a single frame of the subject, and wherein with the rule-based pose inference model, determining a rule-based output pose of interest by processing the same single image frame, and determining as the output pose of interest the rule-based output pose of interest, if the rule-based pose inference model is able to determine the rule-based output pose of interest, otherwise determining the data-driven pose of interest as the output pose of interest.

Abstract: A method and system for pose estimation includes receiving at least one image frame captured by an imaging device, wherein the imaging device is arranged to image at least one subject; determining one or more candidate positions for each of a plurality of human keypoints, wherein each candidate position is associated with a likelihood that a human keypoint is located at such position; generating one or more combinations of human keypoints based on the determined one or more candidate positions; and determining a pose of each of the at least one subject based at least on the one or more generated combinations of human keypoints.