Model-based Human Pose Estimation and
Tracking
Abstract
Estimating human body poses in static images and
tracking poses in monocular video are important for many image and video
understanding applications including video surveillance. The problem is
difficult due to the presence of image noise, ambiguities in image observation,
high dimensional state space and partial occlusion of the human body. Human pose
estimation and tracking can be made more robust by integrating the detection of
components such as face and limbs. An approach based on data-driven Markov chain
Monte Carlo (DD-MCMC) is used, where component detection results generate state
proposals for pose estimation and initialization. Experimental results on
a set of test images and indoor video sequences show that the method is able to
estimate the pose in realistic scenarios.