@inproceedings{DaemenHerderKochetal.2016, author = {Daemen, Jeff and Herder, Jens and Koch, Cornelius and Ladwig, Philipp and Wiche, Roman and Wilgen, Kai}, title = {Semi-Automatic Camera and Switcher Control for Live Broadcast}, series = {TVX '16 Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, Chicago, Illinois, USA — June 22 - 24, 2016}, booktitle = {TVX '16 Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, Chicago, Illinois, USA — June 22 - 24, 2016}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-4067-0}, doi = {10.1145/2932206.2933559}, pages = {129 -- 134}, year = {2016}, abstract = {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.}, language = {en} } @inproceedings{BallesterRipollHerderLadwigetal.2016, author = {Ballester Ripoll, Marina and Herder, Jens and Ladwig, Philipp and Vermeegen, Kai}, title = {Comparison of two Gesture Recognition Sensors for Virtual TV Studios}, series = {GI-VRAR, Workshop Proceedings / Tagungsband: Virtuelle und Erweiterte Realit{\"a}t - 13. Workshop der GI-Fachgruppe VR/AR,}, booktitle = {GI-VRAR, Workshop Proceedings / Tagungsband: Virtuelle und Erweiterte Realit{\"a}t - 13. Workshop der GI-Fachgruppe VR/AR,}, editor = {Pfeiffer, Thies and Fr{\"o}hlich, Julia and Kruse, Rolf}, publisher = {Shaker Verlag}, address = {Herzogenrath}, isbn = {978-3-8440-4718-9}, year = {2016}, abstract = {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.}, language = {en} }