Abstract

This tutorial introduces state-of-the-art methods for tracking multiple spatially extended objects based on unlabeled noisy point clouds, e.g., from radar or lidar sensors. In the first part, the focus lies on tracking a single extended object, i.e., the objective is to simultaneously estimate the shape and position of a moving object based on spatially distributed noisy detections. Model-based approaches for tracking elliptical and star-convex shaped objects are treated. Furthermore, recent learning-based approaches are discussed, which learn the spatial distribution of detections from real data. The second part considers the track management and data association problem, i.e., the initialization and termination of tracks as well as the association of detections to objects. After an in-depth analysis of the data association problem for extended objects, various data association approaches are discussed. Finally, recent frameworks for multiple extended object tracking are introduced such as the random finite set-based Poisson multi-Bernoulli mixture framework.