@misc{GedatFechnerFiebelkornetal.2018, author = {Gedat, Egbert and Fechner, Pascal and Fiebelkorn, Richard and Vandenhouten, Jan and Vandenhouten, Ralf}, title = {Image recognition of multi-perspective data for intelligent analysis of gestures and actions}, series = {Wissenschaftliche Beitr{\"a}ge 2018}, volume = {22}, journal = {Wissenschaftliche Beitr{\"a}ge 2018}, issn = {0949-8214}, doi = {10.15771/0949-8214_2018_3}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-10230}, pages = {25 -- 30}, year = {2018}, abstract = {The BERMUDA project started in January 2015 and was successfully completed after less than three years in August 2017. A technical set-up and an image processing and analysis software were developed to record and evaluate multi-perspective videos. Based on two cameras, positioned relatively far from one another with tilted axes, synchronized videos were recorded in the laboratory and in real life. The evaluation comprised the background elimination, the body part classification, the clustering, the assignment to persons and eventually the reconstruction of the skeletons. Based on the skeletons, machine learning techniques were developed to recognize the poses of the persons and next for the actions performed. It was, for example, possible to detect the action of a punch, which is relevant in security issues, with a precision of 51.3 \% and a recall of 60.6 \%.}, language = {en} } @inproceedings{PulwerFiebelkornZeschetal.2019, author = {Pulwer, Silvio and Fiebelkorn, Richard and Zesch, Christoph and Steglich, Patrick and Villringer, Claus and Villasmunta, Francesco and Gedat, Egbert and Handrich, Jan and Schrader, Sigurd and Vandenhouten, Ralf}, title = {Endoscopic orientation by multimodal data fusion}, series = {Proc. SPIE 10931, MOEMS and Miniaturized Systems XVIII}, booktitle = {Proc. SPIE 10931, MOEMS and Miniaturized Systems XVIII}, issn = {1996-756X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-11420}, year = {2019}, abstract = {To improve the feasibility of endoscopic inspection processes we developed a system that provides online information about position, orientation and viewing direction of endoscopes, to support the analysis of endoscopic images and to ease the operational handling of the equipment. The setup is based on an industrial endoscope consisting of a camera, various MEMS and multimodal data fusion. The software contains algorithms for feature and geometric structure recognition as well as Kalman filters. To track the distal end of the endoscope and to generate 3D point cloud data in real time the optical and photometrical characteristics of the system are registered and the movement of the endoscope is reconstructed by using image processing techniques.}, language = {en} } @misc{HandrichVandenhouten2019, author = {Handrich, Jan and Vandenhouten, Ralf}, title = {Erweiterung eines landmarkenbasierten Innenraumortungsverfahrens f{\"u}r Mobilger{\"a}te mit ARCore}, series = {Wissenschaftliche Beitr{\"a}ge 2019}, volume = {23}, journal = {Wissenschaftliche Beitr{\"a}ge 2019}, issn = {0949-8214}, doi = {10.15771/0949-8214_2019_4}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-10790}, pages = {27 -- 32}, year = {2019}, abstract = {Die Lokalisierung in Innenr{\"a}umen, die sich nicht auf Satellitennavigation wie GPS verlassen kann, ist nach wie vor eine Herausforderung. Viele Methoden zur Lokalisierung in Innenr{\"a}umen beruhen auf Funksignalen oder anderen Sendern (z.B. Infrarot), die eine kostenintensive Infrastruktur erfordern, die in einem Geb{\"a}ude installiert werden muss. Außerdem sind diese Techniken ungenau, wenn Reflexionen der Signale in einem Geb{\"a}ude auftreten. Die Forschungsgruppe Telematik der TH Wildau hat f{\"u}r diese Problemstellung ein optisches Verfahren zur Innenraumlokalisierung entwickelt, das die Kamera eines mobilen Ger{\"a}ts verwendet. Dieser Ansatz erfordert nur die Verf{\"u}gbarkeit von mehreren identifizierbaren Landmarken, die kosteng{\"u}nstig in einem Geb{\"a}ude installiert werden k{\"o}nnen. In diesem Beitrag soll eine M{\"o}glichkeit vorgestellt werden, dieses landmarkenbasierte Verfahren mit der Augmented Reality-Bibliothek ARCore von Google zu erweitern, um eine kontinuierliche Positionsermittlung zu erm{\"o}glichen, auch wenn vor{\"u}bergehend keine Landmarken durch die Ger{\"a}tekamera erfasst ­werden k{\"o}nnen.}, language = {de} }