Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
D. Fritz, A. Mossel, H. Kaufmann:
"Evaluating RGB+D Hand Posture Detection Methods for Mobile 3D Interaction";
Vortrag: 16th International Conference and Exibition on Virtual Technologies,
Laval, France;
09.04.2014
- 11.04.2014; in: "Proceedings of the 16th International Conference of Virtual Technologies (VRIC'14)",
ACM,
(2014),
ISBN: 978-1-4503-2626-1.
Kurzfassung englisch:
In mobile applications it is crucial to provide intuitive means for 2D and 3D interaction. A large number of techniques exist to support a natural user interface (NUI) by detecting the userīs hand posture in RGB+D (depth) data. Depending on a given interaction scenario, each technique hast its advantages and disadvantages. To evaluate the performance of the various techniques on a mobile device, we conducted a systematic study by comparing the accuracy of five common posture recognition approaches with varying illumination and background. To be able to perform this study, we developed a powerful hard- and software framework that is capable of processing and fusing RGB and depth data directly on a handheld device. Overall results reveal best recognition rate of posture detection for combined RGB+D data at the expense of update rate. Finally, to support users in choosing the appropriate technique for their specific mobile interaction task, we derived guidelines based on our study.
Schlagworte:
Natural User Interface, RGB+D Hand Posture Detection, Handheld Augmented Reality, Comparative Study
Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/PubDat_227113.pdf