Learning Instead of Markers: Flexible Recognition of Mobile Devices on Interactive Surfaces
We propose an approach for recognition of mobile devices on interactive surfaces that do not support optical markers. Our system only requires an axis aligned bounding box of the object placed on the touchscreen in combination with position data from the mobile devices integrated inertia measurement unit (IMU). We put special emphasis on maximum flexibility in terms of compatibility with varying multi-touch sensor techniques and different kinds of mobile devices. A new device can be added to the system with a short training phase, during which the device is moved across the interactive surface. A device model is automatically created from the recorded data using support vector machines. Different devices of the same size are identified by analyzing their IMU data streams for transitions into a horizontally resting state. The system has been tested in a museum environment.
KeywordsHuman computer interaction Tabletop interaction Markerless object tracking Machine learning
Philipp Mock was a doctoral student of the LEAD Graduate School [GSC1028], funded by the Excellence Initiative of the German federal and state governments.
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