000 Informatik, Informationswissenschaft, allgemeine Werke
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The article discusses the question of “How to convey the experience in a virtual environment to third parties?” and explains the different technical implementations which can be used for live streaming and recording of a mixed reality experience. The real-world applications of our approach include education, entertainment, e- sports, tutorials, and cinematic trailers, which can benefit from our research by finding a suitable solution for their needs. We explain and outline our Mixed Reality systems as well as discuss the experience of recorded demonstrations of different VR applications, including the need for calibrated camera lens parameters based on realtime encoder values.
Live video broadcasting requires a multitude of professional expertise to enable multi-camera productions. Robotic systems allow the automation of common and repeated tracking shots. However, predefined camera shots do not allow quick adjustments when required due to unpredictable events. We introduce a modular automated robotic camera control and video switch system, based on fundamental cinematographic rules. The actors' positions are provided by a markerless tracking system. In addition, sound levels of actors' lavalier microphones are used to analyse the current scene. An expert system determines appropriate camera angles and decides when to switch from one camera to another. A test production was conducted to observe the developed prototype in a live broadcast scenario and served as a video-demonstration for an evaluation.
Live-Video-Broadcasting mit mehreren Kameras erfordert eine Vielzahl von Fachkenntnissen. Robotersysteme ermöglichen zwar die Automatisierung von gängigen und wiederholten Tracking-Aufnahmen, diese erlauben jedoch keine kurzfristigen Anpassungen aufgrund von unvorhersehbaren Ereignissen. In diesem Beitrag wird ein modulares, automatisiertes Kamerasteuerungs- und Bildschnitt-System eingeführt, das auf grundlegenden kinematografischen Regeln basiert. Die Positionen der Akteure werden durch ein markerloses Tracking-System bereitgestellt. Darüber hinaus werden Tonpegel der Lavaliermikrofone der Akteure zur Analyse der aktuellen Szene verwendet. Ein Expertensystem ermittelt geeignete Kamerawinkel und entscheidet, wann von einer Kamera auf eine andere umgeschaltet werden soll. Eine Testproduktion wurde durchgeführt, um den entwickelten Prototyp in einem Live-Broadcast-Szenario zu beobachten und diente als Videodemonstration für eine Evaluierung.
In order to improve the interactivity between users and computers, recent technologies focus on incorporating gesture recognition into interactive systems. The aim of this article is to evaluate the effectiveness of using a Myo control armband and the Kinect 2 for recognition of gestures in order to interact with virtual objects in a weather report scenario. The Myo armband has an inertial measurement unit and is able to read electrical activity produced by skeletal muscles, which can be recognized as gestures, which are trained by machine learning. A Kinect sensor was used to build up a dataset which contains motion recordings of 8 different gestures and was also build up by a gesture training machine learning algorithm. Both input methods, the Kinect 2 and the Myo armband, were evaluated with the same interaction patterns in a user study, which allows a direct comparison and reveals benefits and limits of each technique.