Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of an extended object and discuss its delimitation to other object types and sensor models. Next, different shape models and possibilities to model the number of measurements are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking methods – the random matrix approach and random hypersurface approach. The next part treats approaches for tracking multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where three example applications involving LIDAR, RGB, and RGB-D sensors are highlighted.