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Im Rahmen des diesjährigen Workshop Automotive HMI werden wieder eine Vielzahl an Vorträgen aus dem Bereich automobiler Mensch-Maschine Schnittstellen präsentiert. Des Weiteren ist wie in den beiden letzten Jahren ein Interaktiver Innovationsworkshop Teil des Programms. Das Motto der Mensch und Computer 2014 lautet „Interaktiv Unterwegs “. Dies passt hervorragend zum Thema des Workshops.
We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios.
In the context of existing approaches to cluster computing we present a newly developed modular framework `SimpleHydra' for rapid deployment and management of Beowulf clusters. Instead of focusing only the pure computation tasks on homogeneous clusters (i.e. clusters with identically set up nodes), this framework aims to ease the configuration of heterogeneous clusters and to provide a low-level / high-level object-oriented API for low-latency distributed computing. Our framework does not make any restrictions regarding the hardware and minimizes the use of external libraries to the case of special modules. In addition to that our framework enables the user to develop highly dynamic cluster topologies. We describe the framework's general structure as well as time critical elements, give application examples in the `Big-Data' context during a research project and briefly discuss additional features. Furthermore we give a thorough theoretical time/space complexity analysis of our implemented methods and general approaches.
Handgesten im Automobil haben das Potenzial einer Kombination von gut sichtbaren Displays nahe der Windschutzscheibe und einer als intuitiv empfundenen Gestensteuerung, wie sie berührungsgesteuert von Smartphones aber auch berührungslos von einigen Fernsehgeräten bekannt ist. Bei entsprechender Positionierung der Sensoren können so die Augen auf der Straße und die Hände am Lenkrad oder zumindest sehr nahe dazu verbleiben. Der hier beschriebene frühe Demonstrator zeigt die Machbarkeit dieser Technologie mit einem neuartigen Erkennungsverfahren.
In recent years, teachers have started to conduct pedagogical activities to promote different kinds of learning interactions supported by rich media. The deployment of such activities is rapidly increasing, as teachers and students own technological means that allow supporting them along such interactions. These activities can be carried out in traditional classroom settings while using regular computers. Additionally, they can also be conducted from anywhere at any time while using smartphones and tablets. In this paper, we describe a pedagogical activity requiring students to author and later peer- assess learning interactions
incorporated to videos in YouTube. We describe EDU.Tube, an environment that enables them to create, share and consume such rich media learning activities across a variety of devices. We then detail a plan for the implementation of an activity that took place in 3 different classes dealing with diverse materials addressing computer science related topics. Finally, we also
provide an evaluation presenting students' insights and feedbacks resulting from the experienced activity. We discuss and analyze these outcomes in order to elaborate on them as concerns that could be applied for the further deployment of the EDU.Tube environment.
In this paper, we describe an efficient method for a fast people re-identification based on models of human clothes. An initial model is estimated during people detection and tracking, which will be refined during the re-identification. This stepwise extraction, combination and comparing of features speeds up the whole re-identification. For the refining, several saliency maps are used to extract individual features. These individual features are located separately for any human body part. The body parts are located with an optimized GPU-based HOG detector. Furthermore, we introduce a meanshift-based fusion concept which utilizes multiple detectors in order to increase the detection reliability.
In den letzten Jahren ist die Verwendung mobiler Endgeräte im Automotive Bereich immer wichtiger geworden. Auf der einen Seite bringen immer mehr Personen ihre mobilen Geräte mit in ihr Auto und wollen hier auch auf verschiedene Funktionen des jeweiligen mobilen Geräts zugreifen können. Auf der anderen Seite haben sich mobile Geräte und die dort zum Einsatz kommenden Betriebssysteme aber auch als ideale Kandidaten für eine IT Unterstützung im Automotive Bereich herausgestellt. Das Ziel dieses Beitrages ist es, erste Erfahrungen aus der Entwicklung eines Infotainmentsystems auf Basis einer Android basierten Hardware vorzustellen.