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Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)
Aachen University, Aachen, Germany. (Computer Vision Group, RWTH Aachen)
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0002-2953-1564
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS)ORCID iD: 0000-0003-0217-9326
2015 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2015, 697-703 p.Conference paper, (Refereed)
Abstract [en]

We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 697-703 p.
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keyword [en]
Human Detection, Robot Vision, Industrial Safety
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-47340DOI: 10.1109/ICRA.2015.7139255ISI: 000370974900101Scopus ID: 2-s2.0-84938245889ISBN: 978-1-4799-6923-4 (print)OAI: oai:DiVA.org:oru-47340DiVA: diva2:891476
Conference
2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, United States, May 26-30, 2015
Available from: 2016-01-07 Created: 2016-01-07 Last updated: 2016-03-29Bibliographically approved

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Mosberger, RafaelAndreasson, HenrikLilienthal, Achim
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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Output format
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