Human pose estimation based on data-driven Monte Carlo hidden Markov models

Abstract

Estimating human poses from 2D images or video sequences can provide the moving trajectories of the body joints for the high level processing, human activity recognition, which is applicable in surveillance, human-computer interaction and clinical and sport analysis. This project proposes a new statistical formulation called the data-driven Monte Carlo hidden Markov model to estimate human poses from random initializations.