Length: 3 hours
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 Linköping 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 Linköping 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 Linköping 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 Göttingen, 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 Göttingen.
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