Intended Audience and Prerequisite Knowledge: The intended audience is academics and professionals with an interest in multiple extended object estimation. Recommended prerequisite knowledge is linear algebra, probability theory and state estimation.
Integration of extended target models in to multiple point target tracking frameworks will only be briefly outlined; focus will be on dedicated multiple extended target tracking frameworks, especially Random Finite Set (RFS) based algorithms.
Attendees that are unfamiliar with RFS algorithms can acquire basic knowledge by attending either the tutorial “A 2 Finite-Set Statistics Prime” by R. Mahler or the tutorial “Implementations of random-finite-set-based multi-target filters” by B.-N. Vo and B.-T. Vo.

Description: Autonomous driver safety functions are standard in many modern cars, and semi-automated systems (e.g., traffic jam assist) are becoming more and more common. Construction of a driverless vehicle requires solutions to many different problems, among them multiple object tracking. This tutorial will introduce the audience to extended object tracking, i.e., object tracking using modern high resolution sensors that give multiple detections per object. State of the art theory will be introduced, and relevant real world applications will be shown where different object types—e.g., pedestrians, bicyclists, cars—are tracked using different sensors such as lidar, radar, and camera.

Presenter: Karl Granström, Stephan Reuter, and Marcus Baum

Karl Granström is a postdoctoral research fellow at the Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden. He received the MSc degree in Applied Physics and Electrical Engineering in May 2008, and the PhD degree in Automatic Control in November 2012, both from Link¨oping University, Sweden. He previously held postdoctoral positions at the Department of Electrical and Computer Engineering at University of Connecticut, USA, from September 2014 to August 2015, and at the Department of Electrical Engineering of Link¨oping University from December 2012 to August 2014. His research interests include estimation theory, multiple model estimation, sensor fusion and target tracking, especially for extended targets. He has received paper awards at the Fusion 2011 and Fusion 2012 conferences.
Karl Granstr¨om has 8 years experience teaching courses on automatic control, digital signal processing and sensor fusion at Link¨oping University, and in 2012 he co-organized a tutorial about multiple target tracking at the European Microwave Week (EuMW 2012) in Amsterdam, Netherlands.

Stephan Reuter received the Diploma degree (equivalent to M.Sc. degree) and the Dr.-Ing. degree (equivalent to Ph.D.) in electrical engineering from Ulm University, Germany, in 2008 and 2014, respectively. Since 2008 he is a research assistant at the Institute of Measurement, Control and Microtechnology at Ulm University. He received the best Student Paper award at FUSION 2014 and the Uni-DAS award for an excellent PhD thesis in the field of driver assistance systems. His main research topics are sensor data fusion, multi-object tracking, extended object tracking, environment perception for intelligent vehicles, and sensor data processing.
Stephan Reuter has 7 years experience in teaching courses on control engineering, localization and tracking.

Marcus Baum is Juniorprofessor (Assistant Professor) at the University of G¨ottingen, Germany. He received the Diploma degree in computer science from the University of Karlsruhe (TH), Germany, in 2007, and graduated as Dr.-Ing. (Doctor of Engineering) at the Karlsruhe Institute of Technology (KIT), Germany, in 2013. From 2013 to 2014, he was postdoc and assistant research professor at the University of Connecticut, CT, USA. His research interests are in the field of data fusion, estimation, and tracking. Marcus Baum is associate administrative editor of the ”Journal of Advances in Information Fusion (JAIF)” and serves as local arrangement chair of the ”19th International Conference on Information Fusion (FUSION 2016)”. He received the best student paper award at the FUSION 2011 conference.
Marcus Baum has 8 years experience in teaching courses on sensor data fusion, localization and tracking at several universities. Currently, he is teaching a graduate course on ”Sensor Data Fusion” at the University of G¨ottingen.