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Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
2008 (English)In: Proceedings of the IEEE Aerospace Conference, IEEE , 2008, 1-8Conference paper (Refereed)
Abstract [en]

Recent advances in the field of Unmanned Aerial Vehicles (UAVs) make flying robots suitable platforms for carrying sensors and computer systems capable of performing advanced tasks. This paper presents a technique which allows detecting humans at a high frame rate on standard hardware onboard an autonomous UAV in a real-world outdoor environment using thermal and color imagery. Detected human positions are geolocated and a map of points of interest is built. Such a saliency map can, for example, be used to plan medical supply delivery during a disaster relief effort. The technique has been implemented and tested on-board the UAVTech1 autonomous unmanned helicopter platform as a part of a complete autonomous mission. The results of flight- tests are presented and performance and limitations of the technique are discussed.

Aerospace Conference Proceedings, ISSN 1095-323X ; 2008
National Category
Computer Science
URN: urn:nbn:se:liu:diva-44654DOI: 10.1109/AERO.2008.4526559Local ID: 77239ISBN: 978-1-4244-1488-8 (online)ISBN: 978-1-4244-1487-1 (print)OAI: diva2:265516
The IEEE Aerospace Conference, March 1-8, Big Sky, Montana, USA
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-08-19Bibliographically approved
In thesis
1. Increasing Autonomy of Unmanned Aircraft Systems Through the Use of Imaging Sensors
Open this publication in new window or tab >>Increasing Autonomy of Unmanned Aircraft Systems Through the Use of Imaging Sensors
2011 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors.

First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings.

Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.

Publisher, range
Linköping: Linköping University Electronic Press, 2011. 96 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1510
UAV, UAS, UAV autonomy, human-body detection, color-thermal image fusion, vehicle tracking, geolocation, UAV indoor navigation
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:liu:diva-71295 (URN)LiU-Tek-Lic-2011:49 (Local ID)978-91-7393-034-5 (ISBN)LiU-Tek-Lic-2011:49 (Archive number)LiU-Tek-Lic-2011:49 (OAI)
2011-11-04, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2011-11-28 Created: 2011-10-10 Last updated: 2011-11-28Bibliographically approved

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Rudol, PiotrDoherty, Patrick
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