A Robust, Real-Time Capable Framework for Fully Automated Order Picking of Pouch-Parceled Goods

  • Adrian BöckenkampEmail author
  • Frank Weichert
  • Christian Prasse
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


The automatic handling of pouch-parceled goods is still challenging because the fillings cause almost random variations of the pouch geometry during conveyance. This paper presents a novel vision-based detection and tracking framework for robustly localizing and tracking pouch-parceled goods in real-time. It is exemplarily integrated in a Cover Belt Conveyor (CBC) system to decide whether and when pouches have to be separated. The core concepts include the estimation of the current pouch velocity using sparse optical flow, the detection and tracking of custom markers by means of structural image analysis and supervised classification, the verification of contextual constraints, and, eventually, the execution of a cutting strategy. The presented concepts tackle the general challenges superimposed by pouch packagings and are thus applicable in any other related automation scenarios as well. An average per-frame processing time of less than 20 ms, an averaged detection accuracy of 99.93% and a cutting accuracy less than 8 mm prove the real-time capabilities, robustness and applicability of the approach.


Real-time framework Fiducial marker Image analysis Supervised classification Optical flow 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Adrian Böckenkamp
    • 1
    Email author
  • Frank Weichert
    • 1
  • Christian Prasse
    • 2
  1. 1.Department of Computer Science VIITechnical University of DortmundDortmundGermany
  2. 2.Fraunhofer Institute for Material Flow and LogisticsDortmundGermany

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