Automatic Detection of Objects Blocking Elevator Doors using Computer Vision

David Baumgartner, Ignace Jordens, Daniel Wilfing, Oliver Krauss, Gerald Adam Zwettler

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitrag

Abstract

In this paper we present a new approach applying computer vision methods to image data acquired with depth perception cameras to map the interior of the elevator, detect the position and the state of the door and to detect objects in the door area. The depth data is used to determine the elevator cabin as safety cube, i.e. the position of the door, layout of the elevator and so on, while color data further enhances the detection of new objects. The approach can detect the state of the elevator door as either opened or closed, while no object is blocking the view to the door, as well as successfully identify objects blocking an open door. This elevator monitoring proves to be relevant for determination of the elevator state, safety as well aspects of predictive maintenance.
OriginalspracheEnglisch
TitelProceedings of the 23rd International Congress on Vertical Transportation Technologies
Seitenumfang12
PublikationsstatusIn Vorbereitung - 2020
VeranstaltungThe 23rd International Congress on Vertical Transportation Technologies - Prague, Tschechische Republik
Dauer: 16 Jun 202018 Jun 2020
http://www.elevcon.com/

Konferenz

KonferenzThe 23rd International Congress on Vertical Transportation Technologies
Land/GebietTschechische Republik
OrtPrague
Zeitraum16.06.202018.06.2020
Internetadresse

Schlagwörter

  • Elevator Monitoring
  • Lucas Kanade Optical Flow
  • Depth Camera
  • Reflection Resistance
  • Object Detection
  • Safety
  • Predictive Maintenance

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