  In this paper, we present a data-driven approach for human pose tracking in
video data. We formulate the human pose tracking problem as a discrete
optimization problem based on spatio-temporal pictorial structure model and
solve this problem in a greedy framework very efficiently. We propose the model
to track the human pose by combining the human pose estimation from single
image and traditional object tracking in a video. Our pose tracking objective
function consists of the following terms: likeliness of appearance of a part
within a frame, temporal displacement of the part from previous frame to the
current frame, and the spatial dependency of a part with its parent in the
graph structure. Experimental evaluation on benchmark datasets (VideoPose2,
Poses in the Wild and Outdoor Pose) as well as on our newly build ICDPose
dataset shows the usefulness of our proposed method.
