Extended Object Tracking with Random Hypersurface Models

TL;DR

This paper introduces the Random Hypersurface Model (RHM) for extended object tracking, enabling simultaneous estimation of an object's shape and motion using a Gaussian estimator, with specific methods for elliptic and star-convex shapes.

Contribution

The paper presents the RHM framework that jointly estimates shape and kinematic state, extending traditional tracking models to include shape approximation.

Findings

Effective shape estimation for elliptic and star-convex objects

Gaussian estimators derived for shape and motion parameters

Improved extended object tracking accuracy

Abstract

The Random Hypersurface Model (RHM) is introduced that allows for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.