Scalable Detection and Tracking of Extended Objects

Abstract

This paper presents a factor graph formulation and sum-product algorithm (SPA) for scalable detection and tracking of extended objects that generate multiple measurements. The proposed method dynamically introduces newly detected objects into the state space and efficiently performs probabilistic multiple-measurement to object associations. We demonstrate performance advantages of our method numerically in a challenging tracking scenario with multiple closely-spaced extended objects.