Since 2016, we have been organizing tutorial presentations and workshops at several international conferences. The next tutorial workshop will be held on June 5, 2022 at the IEEE Intelligent Vehicles Symposium (IV 2022) in Aachen, Germany, see

Location & Time

Room B003, ika, RWTH Aachen University (morning session)

June, 5, 2022


  • 09:00 am: Introduction (Jens Honer, Marcus Baum)
  • 09:15 am: Multiple Extended Object Tracking (Jens Honer)
  • 10:45 am: Break
  • 11:00 am: Single Extended Object Tracking (Marcus Baum)
  • 12:30 pm: Discussion
  • 01:00 pm: End


Marcus Baum
University of Goettingen
Group Page
Jens Honer

Former Co-Organizers:

  • Karl Granström (2016 – 2021)
  • Stephan Reuter (2016 – 2017)


In order to safely navigate through traffic, an automated vehicle needs to be aware of the trajectories and dimensions of all dynamic objects (e.g., traffic participants) as well as the locations and dimensions of all stationary objects (e.g., road infrastructure). For this purpose, automated vehicles are equipped with modern high-resolution sensors like LIDAR, RADAR or cameras that allow to detect objects in the vicinity. Typically, the sensors generate multiple detections for each object, where the detections are unlabeled, i.e. it is unknown which of the objects was detected. Furthermore, the detections are corrupted by sensor noise, e.g., some detections might be clutter, and some detections might be missing. The task of detecting and tracking an unknown number of moving spatially extended objects (e.g., traffic participants) based on noise-corrupted unlabeled measurements is called multiple extended object tracking. This tutorial will introduce state-of-the-art theory for multiple extended object tracking together with relevant real-world automotive applications. In particular, we will demonstrate applications for different object types, e.g., pedestrians, bicyclists, and cars, using different sensors such as LIDAR, RADAR, and camera.


  • Multiple extended object tracking:
    • Modeling multiple extended object tracking with random finite sets
    • Bayes’ Theorem with random finite sets
    • Approximation schemes for Bayes’ Theorem and tractable random set filters
    • Connection between multi-target tracking and extended target tracking
  • Single extended object tracking:
    • Models and methods for tracking elliptical and star-convex approximations of extended objects
    • Learning-based approaches for extended object tracking
    • Classification of extended objects
    • Multi-sensor fusion for extended objects

Previous Tutorials

  • 2021 IEEE Intelligent Vehicles Symposium, Nagoya, Japan, Virtual
  • 2019 International Conference on Information Fusion, Ottawa, Canada
  • 2019 IEEE Intelligent Vehicles Symposium, Paris, France
  • 2018 International Conference on Information Fusion, Cambridge, UK
  • 2017 International Conference on Information Fusion, Xian, China
  • 2017 IEEE Intelligent Vehicles Symposium, Los Angelos, USA
  • 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Baden-Baden, Germany
  • 2016 International Conference on Information Fusion 2016, Heidelberg, Germany
  • 2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden