Network Flow Labeling for Extended Target Tracking PHD Filters

Abstract

The Probability Hypotheses Density (PHD) filter is a method for tracking multiple target objects based on unlabeled detections. However, as the PHD filter employs a first-order approximation of random finite sets, it does not provide track labels, i.e., targets of consecutive time steps are not associated with each other. In this work, an intuitive and efficient labeling strategy on top of the extended target PHD filter is proposed. The approach is based on solving a network flow problem and makes use of the Wasserstein metric to account for the spatial extent of the objects. The resulting tracker is evaluated with laser scanner data from two traffic scenarios.