Image moment-based random hypersurface model for extended object tracking
Gang Yao, Ashwin P. Dani · 2017
In this paper, a novel image moment-based model for extended object shape estimation and tracking is presented. A method to represent and estimate an elliptical shape using its image moments is first developed. The model of representing the shape of an object falls under the category of random hypersurface model (RHM) for extended object tracking. The moments are estimated using an unscented Kalman filter (UKF). Measurements of 2D points sampled from the object are used to estimate the shape. A measurement model for the UKF is derived based on the 2D locations of the feature points sampled from the object. Simulation results of the proposed image moment-based model are presented to estimate the shape of static ellipse, and of the moving ellipse.