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| | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px"> | | | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px"> |
− | '''Presenter:''' [mailto:karl.granstrom@chalmers.se Karl Granström], Stephan Reuter, and [mailto:marcus.baum@cs.uni-goettinge.de Marcus Baum]<br /> | + | '''Presenter:''' [mailto:karl.granstrom@chalmers.se Karl Granström], [mailto:stephan.reuter@uni-ulm.de Stephan Reuter], and [mailto:marcus.baum@cs.uni-goettingen.de Marcus Baum]<br /> |
| '''Length:''' 3 hours<br /> | | '''Length:''' 3 hours<br /> |
| '''Brief 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.<br /> | | '''Brief 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.<br /> |