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

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitrag

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.
OriginalspracheEnglisch
TitelProceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS) Workshops
Herausgeber (Verlag)VDE Verlag
PublikationsstatusVeröffentlicht - 2010
Veranstaltung23rd International Conference on Architecture of Computing Systems (ARCS) Workshops - Hannover, Deutschland
Dauer: 23 Feb. 201023 Feb. 2010

Workshop

Workshop23rd International Conference on Architecture of Computing Systems (ARCS) Workshops
Land/GebietDeutschland
OrtHannover
Zeitraum23.02.201023.02.2010

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