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A smart data annotation tool for multi-sensor activity recognition
Diete, Alexander
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Sztyler, Timo
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1109/PERCOMW.2017.7917542
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URL:
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http://ieeexplore.ieee.org/document/7917542/
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Weitere URL:
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http://publications.wim.uni-mannheim.de/informatik...
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2017
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Buchtitel:
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2017 IEEE International Conference on Pervasive Computing and Communications Workshops : PERCOM Workshops
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Seitenbereich:
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111-116
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Veranstaltungstitel:
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ARDUOUS '17: 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems : affiliated to IEEE PerCom 2017
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Veranstaltungsort:
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Kona, Big Island, HI
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Veranstaltungsdatum:
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March 17, 2017
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Herausgeber:
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Yordanova, Kristina
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Ort der Veröffentlichung:
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Piscataway, NJ
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Verlag:
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IEEE Computer Soc.
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ISBN:
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978-1-5090-4339-2 , 978-1-5090-4338-5
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Fachgebiet:
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004 Informatik
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Abstract:
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Annotation of multimodal data sets is often a time consuming and a challenging task as many approaches require a accurate labeling. This includes in particular video recordings as often labeling exact to a frame is required. For that purpose, we created an annotation tool that enables to annotate data sets of video and inertial sensor data. However, in contrast to the most existing approaches, we focus on semi-supervised labeling support by utilizing different sensor types to infer labels for the whole dataset. Hence, after labeling a small set of instances our system is able to provide labeling recommendations and in turn make learning of image features more feasible by vastly speeding up the labeling time for single frames. We aim to rely on the inertial sensors of our wristband to support the labeling of video recording. For that purpose, we apply template matching in context of dynamic time warping to identify time intervals of certain actions. To investigate the feasibility of our approach we focus on a real world scenario, i.e., we gathered a data set which describes an order picking scenario of a logistic company. In this context, we focus mainly on the picking process as the selecting of the correct items can be prone to errors. Preliminary results show that we are able to identify 69% of the grabbing motion periods of time.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Diete, Alexander
;
Sztyler, Timo
;
Stuckenschmidt, Heiner
Google Scholar:
Diete, Alexander
;
Sztyler, Timo
;
Stuckenschmidt, Heiner
ORCID:
Diete, Alexander, Sztyler, Timo and Stuckenschmidt, Heiner  ORCID: https://orcid.org/0000-0002-0209-3859
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