Recording a Complex, Multi Modal Activity Data Set for Context Recognition

Paul Lukowicz, Gerald Pirkl, David Bannach, Florian Wagner, Alberto Calatroni, Kilian Förster, Thomas Holleczek, Mirco Rossi, Daniel Roggen, Gerhard Tröster, Jakob Doppler, Clemens Holzmann, Andreas Riener, Alois Ferscha, Ricardo Chavarriaga

Research output: Chapter in Book/Report/Conference proceedingsConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS) Workshops
PublisherVDE Verlag
Publication statusPublished - 2010
Event23rd International Conference on Architecture of Computing Systems (ARCS) Workshops - Hannover, Germany
Duration: 23 Feb 201023 Feb 2010

Workshop

Workshop23rd International Conference on Architecture of Computing Systems (ARCS) Workshops
CountryGermany
CityHannover
Period23.02.201023.02.2010

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