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Data fusion of multi-spectral cameras on a low-power processing platform for self-sufficient outdoor operation

 
: Reichel, Andreas; Peter, Nico; Döge, Jens; Priwitzer, Holger; Kasper, André; Ludwig, Mike; Ziems, Bernd

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Fulltext ()

Rosenberger, M. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Photonics and Education in Measurement Science 2019 : 17-19 September 2019, Jena, Germany
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 11144)
ISBN: 978-1-5106-2981-3
ISBN: 978-1-5106-2982-0
Paper 111440Y, 9 pp.
Conference "Photonics and Education in Measurement Science" <2019, Jena>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
KMU-innovativ; 01IS15030B; SmartFusionCam
Mobiles Multi-domain-Kamerasystem mit integrierter Merkmalsextraktion
English
Conference Paper, Electronic Publication
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()

Abstract
Multi-spectral camera set-ups may generally allow for creating surveillance applications even under unfavorable conditions, such as low-light environments or scenes involving vastly different lighting conditions. A high- resolution color camera, a high-dynamic-range camera and an infrared thermal camera were combined into a self-sufficient platform for continuous outdoor operation. The sheer amount of produced data poses a serious challenge, both in terms of available bandwidth and processing power, because self-sufficiency requires using relatively low-power components, and privacy, as high-resolution, multi-spectral image data are sensitive information. Thus, relevant objects of interest had to be efficiently extracted, tracked and georeferenced on the sensor platform. These data, from one or more sensorheads, are then sent via WLAN or mobile data link to a central control unit, possibly anonymized, e.g. prompting immediate action by a human operator in a disaster response use case, or stored for further offline analysis when used in the framework of "Smart City". Applying the classic stereo vision approach would require calibrating both intrinsic and extrinsic parameters of all cameras. The input data's multi-spectral nature complicates the correspondence problem for extrinsic parameter calibration and subsequent stereo matching, while intrinsic parameter calibration according to the pinhole camera model is made difficult due to the cameras having to be focused at infinity. However, by making certain reasonable assumptions about the observed scene in typical use cases, accepting a possible loss in localization accuracy, camera calibration could be limited to the bare minimum and less computational power was required at run-time.

: http://publica.fraunhofer.de/documents/N-566089.html