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Rutinowski J, Youssef H, Gouda A, Reining C, Roidl M (2022). The Potential of Deep Learning based Computer Vision in Warehousing Logistics. Logistics Journal : Proceedings, Vol. 2022. (urn:nbn:de:0009-14-56018)

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%0 Journal Article
%T The Potential of Deep Learning based Computer Vision in Warehousing Logistics
%A Rutinowski, Jérôme
%A Youssef, Hazem
%A Gouda, Anas
%A Reining, Christopher
%A Roidl, Moritz
%J Logistics Journal : Proceedings
%D 2022
%V 2022
%N 18
%@ 2192-9084
%F rutinowski2022
%X This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.
%L 620
%K Computer Vision
%K Deep Learning
%K Object Segmentation
%K Objekt Segmentierung
%K Pose Estimation
%K Re-Identification
%K Re-Identifikation
%R 10.2195/lj_proc_rutinowski_en_202211_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-56018
%U http://dx.doi.org/10.2195/lj_proc_rutinowski_en_202211_01
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Bibtex

@Article{rutinowski2022,
  author = 	"Rutinowski, J{\'e}r{\^o}me
		and Youssef, Hazem
		and Gouda, Anas
		and Reining, Christopher
		and Roidl, Moritz",
  title = 	"The Potential of Deep Learning based Computer Vision in Warehousing Logistics",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2022",
  volume = 	"2022",
  number = 	"18",
  keywords = 	"Computer Vision; Deep Learning; Object Segmentation; Objekt Segmentierung; Pose Estimation; Re-Identification; Re-Identifikation",
  abstract = 	"This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_proc_rutinowski_en_202211_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-56018"
}
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RIS

TY  - JOUR
AU  - Rutinowski, Jérôme
AU  - Youssef, Hazem
AU  - Gouda, Anas
AU  - Reining, Christopher
AU  - Roidl, Moritz
PY  - 2022
DA  - 2022//
TI  - The Potential of Deep Learning based Computer Vision in Warehousing Logistics
JO  - Logistics Journal : Proceedings
VL  - 2022
IS  - 18
KW  - Computer Vision
KW  - Deep Learning
KW  - Object Segmentation
KW  - Objekt Segmentierung
KW  - Pose Estimation
KW  - Re-Identification
KW  - Re-Identifikation
AB  - This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-56018
DO  - 10.2195/lj_proc_rutinowski_en_202211_01
ID  - rutinowski2022
ER  -
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Wordbib

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ISI

PT Journal
AU Rutinowski, J
   Youssef, H
   Gouda, A
   Reining, C
   Roidl, M
TI The Potential of Deep Learning based Computer Vision in Warehousing Logistics
SO Logistics Journal : Proceedings
PY 2022
VL 2022
IS 18
DI 10.2195/lj_proc_rutinowski_en_202211_01
DE Computer Vision; Deep Learning; Object Segmentation; Objekt Segmentierung; Pose Estimation; Re-Identification; Re-Identifikation
AB This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping.
ER
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Mods

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    <title>The Potential of Deep Learning based Computer Vision in Warehousing Logistics</title>
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  <subject>
    <topic>Computer Vision</topic>
    <topic>Deep Learning</topic>
    <topic>Object Segmentation</topic>
    <topic>Objekt Segmentierung</topic>
    <topic>Pose Estimation</topic>
    <topic>Re-Identification</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2022, Iss. 18
Title

The Potential of Deep Learning based Computer Vision in Warehousing Logistics (eng)
Author Jérôme Rutinowski, Hazem Youssef, Anas Gouda, Christopher Reining, Moritz Roidl
Language eng
Abstract This work describes three deep learning based computer vision approaches, that hold the potential to increase the degree of automation and the productivity of common warehousing procedures. These approaches will focus on: the re-identification of logistical entities, especially when entering and leaving the warehouse; the multi-view pose estimation of logistical entities to track and to localize them on the shop floor; and the category-agnostic segmentation of items in a bin for robotic grasping. Diese Arbeit beschreibt drei Deep-Learning-basierte Computer-Vision-Ansätze, die das Potenzial haben, den Automatisierungsgrad und die Produktivität gängiger Lagerverfahren zu erhöhen. Diese Ansätze konzentrieren sich auf: die Re-Identifizierung von logistischen Einheiten, insbesondere beim Betreten und Verlassen des Lagers; die Multiview-Positionsschätzung von logistischen Einheiten, um sie in der Fabrik zu verfolgen und zu lokalisieren; und die kategorienunabhängige Segmentierung von Artikeln in einem Behälter für das Greifen durch einen Roboter.
Subject Computer Vision, Deep Learning, Object Segmentation, Objekt Segmentierung, Pose Estimation, Re-Identification, Re-Identifikation
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-56018
DOI https://doi.org/10.2195/lj_proc_rutinowski_en_202211_01