Tracking a Person with 3-D Motion by Integrating Optical Flow and Depth
Ryuzo Okada, Yoshiaki Shirai, Jun Miura
Abstract
This paper describes a method of tracking a person with 3-D translation
and rotation by integrating optical flow and depth. The target region
is first extracted based on the probability of each pixel belonging to
the target person. The target state (3-D position, posture, motion) is
estimated by integrating the shape and the position of the target region
in addition to optical flow and depth so that they compensate for each
other, although none of them alone can estimate the 3-D target state
reliably. Multiple target states are maintained when the image measurements
give rise to ambiguities about the target state. Experimental results with
real image sequences show effectiveness of our method.