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Grzeszick R, Feldhorst S, Mosblech C, Fink GA, ten Hompel M (2016). Camera-assisted Pick-by-feel. Logistics Journal : Proceedings, Vol. 2016. (urn:nbn:de:0009-14-44556)

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%0 Journal Article
%T Camera-assisted Pick-by-feel
%A Grzeszick, Réné
%A Feldhorst, Sascha
%A Mosblech, Christian
%A Fink, Gernot A.
%A ten Hompel, Michael
%J Logistics Journal : Proceedings
%D 2016
%V 2016
%N 10
%@ 2192-9084
%F grzeszick2016
%X In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.
%L 620
%K Intralogistics
%K Intralogistik
%K Kommissionieren
%K Kommissionierung
%K Deep Learning
%K Bild Klassifikation
%K Bild Retrieval
%K Aktivitätserkennung
%R 10.2195/lj_Proc_grzeszick_en_201610_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-44556
%U http://dx.doi.org/10.2195/lj_Proc_grzeszick_en_201610_01
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Bibtex

@Article{grzeszick2016,
  author = 	"Grzeszick, R{\'e}n{\'e}
		and Feldhorst, Sascha
		and Mosblech, Christian
		and Fink, Gernot A.
		and ten Hompel, Michael",
  title = 	"Camera-assisted Pick-by-feel",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2016",
  volume = 	"2016",
  number = 	"10",
  keywords = 	"Intralogistics; Intralogistik; Kommissionieren; Kommissionierung; Deep Learning; Bild Klassifikation; Bild Retrieval; Aktivit{\"a}tserkennung",
  abstract = 	"In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_grzeszick_en_201610_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-44556"
}
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RIS

TY  - JOUR
AU  - Grzeszick, Réné
AU  - Feldhorst, Sascha
AU  - Mosblech, Christian
AU  - Fink, Gernot A.
AU  - ten Hompel, Michael
PY  - 2016
DA  - 2016//
TI  - Camera-assisted Pick-by-feel
JO  - Logistics Journal : Proceedings
VL  - 2016
IS  - 10
KW  - Intralogistics
KW  - Intralogistik
KW  - Kommissionieren
KW  - Kommissionierung
KW  - Deep Learning
KW  - Bild Klassifikation
KW  - Bild Retrieval
KW  - Aktivitätserkennung
AB  - In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-44556
DO  - 10.2195/lj_Proc_grzeszick_en_201610_01
ID  - grzeszick2016
ER  -
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Wordbib

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<b:Comments>In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.</b:Comments>
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ISI

PT Journal
AU Grzeszick, R
   Feldhorst, S
   Mosblech, C
   Fink, G
   ten Hompel, M
TI Camera-assisted Pick-by-feel
SO Logistics Journal : Proceedings
PY 2016
VL 2016
IS 10
DI 10.2195/lj_Proc_grzeszick_en_201610_01
DE Intralogistics; Intralogistik; Kommissionieren; Kommissionierung; Deep Learning; Bild Klassifikation; Bild Retrieval; Aktivitätserkennung
AB In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.
ER
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Mods

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    <namePart type="given">Sascha</namePart>
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    <namePart type="given">Christian</namePart>
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  <name type="personal">
    <namePart type="family">Fink</namePart>
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  <name type="personal">
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  <abstract>In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented.</abstract>
  <subject>
    <topic>Intralogistics</topic>
    <topic>Intralogistik</topic>
    <topic>Kommissionieren</topic>
    <topic>Kommissionierung</topic>
    <topic>Deep Learning</topic>
    <topic>Bild Klassifikation</topic>
    <topic>Bild Retrieval</topic>
    <topic>Aktivitätserkennung</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2016, Iss. 10
Title

Camera-assisted Pick-by-feel (eng)
Author Réné Grzeszick, Sascha Feldhorst, Christian Mosblech, Gernot A. Fink, Michael ten Hompel
Language eng
Abstract In this contribution a novel system to support order pickers in warehouses is introduced. In contrast to existing solutions it utilizes the tactile perception in order to reduce the systems impact on the visual and auditive senses. Therefore, a smartwatch and a low-cost camera which are both worn by the picker are combined with activity and object recognition methods for surveying the picking process. The activity recognition is used in order to determine whether an object is picked. Then, barcode detection and a CNN (Convolutional Neural Network) based object recognition approach are employed for recognizing whether the correct item is chosen. Beside the conceptional work, implementation details and evaluation results under realistic conditions and on a publicly available dataset are presented. In diesem Beitrag wird ein neuartiges System zur Unterstützung des manuellen Kommissionierprozesses vorgestellt. Im Gegensatz zu existierenden Systemen wird taktiles Feedback genutzt um den Einfluss auf die audio-visuellen Sinne zu reduzieren. Eine vom Kommissionierer getragene Smartwatch und eine kostengünstige Kamera werden kombiniert mit Methoden der Aktivitätserkennung und der visuellen Objekterkennung, die den Kommissionierprozess überwachen. Zuerst wird mit Hilfe der Aktivitätserkennung bestimmt ob ein Gut vom Kommissionierer gegriffen wird. Im Folgenden werden eine Barcode-Erkennung und eine visuelle Objektklassifikation durch ein tiefes Faltungsnetz genutzt um zu erkennen ob das korrekte Gut gegriffen wurde. Neben der konzeptionellen Ausarbeitung werden Umsetzungsdetails erläutert und abschließend wird eine Auswertung unter realistischen Bedingungen sowie auf einem öffentlich verfügbaren Datensatz vorgestellt.
Subject Intralogistics, Intralogistik, Kommissionieren, Kommissionierung, Deep Learning, Bild Klassifikation, Bild Retrieval, Aktivitätserkennung
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-44556
DOI https://doi.org/10.2195/lj_Proc_grzeszick_en_201610_01