Recording a Complex, Multi Modal Activity Data Set for Context Recognition
P. Lukowicz, G. Pirkl, D. Bannach, F. Wagner, A. Calatroni, K. Förster, T. Holleczek, M. Rossi, D. Roggen, G. Tröster, J. Doppler, C. Holzmann, A. Riener, A. Ferscha, R. Chavarriaga - Recording a Complex, Multi Modal Activity Data Set for Context Recognition - Proceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS) Workshops, Hannover, Deutschland, 2010
In most established fields related to pattern recognition and signal processing standard data sets exist, on which new algorithms can be evaluated and compared. Such data sets ensure that different approaches are compared in a fair and reproducible way. They also allow different groups to concentrate on method development rather then on repeating often considerable effort involved in data collection. Recently publicly available data sets have also started emerging in the area of context recognition (see related work below). However, due to the diversity and complexity of the context recognition domain it is difficult to define a few ”standard” task. Instead, there are many aspects that need to be considered in different applications.