Tracking Partially Occluded Two Dimensional Shapes

Abstract

A method for tracking partially occluded two dimensional polygonal shapes undergoing unknown two dimensional translational and rotational motion has been developed based on Kalman filtering. Observation of a robotic workspace by a machine vision system presents many situations in which known objects may be occluded partially or completely by other objects, fixtures, or the robot itself. Tracking such objects using non-occluded, visible features is an important problem. The method assumes object corners, or some other feature set, can be identified to known accuracy by another technique, and that feature occlusion (absence) can also be detected or recognized. A linear, constant acceleration model is assumed for shape translational and rotation motion in which the shape centroid and angular orientation, as well as their velocities and accelerations, comprise the state. A nonlinear observation model is assumed where the corner or feature locations are measured. The proposed method is investigated under a variety of conditions, including non-constant acceleration, substantial, and total occlusion. Conditions under which tracking is lost are examined.