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An Inexpensive Monocular Vision System for Tracking Humans in Industrial Environments
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-2953-1564
2013 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 5850-5857Conference paper, Published paper (Refereed)
Abstract [en]

We report on a novel vision-based method for reliable human detection from vehicles operating in industrial environments in the vicinity of workers. By exploiting the fact that reflective vests represent a standard safety equipment on most industrial worksites, we use a single camera system and active IR illumination to detect humans by identifying the reflective vest markers. Adopting a sparse feature based approach, we classify vest markers against other reflective material and perform supervised learning of the object distance based on local image descriptors. The integration of the resulting per-feature 3D position estimates in a particle filter finally allows to perform human tracking in conditions ranging from broad daylight to complete darkness.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. p. 5850-5857
Series
Robotics and Automation (ICRA), 2013 IEEE International Conference on, ISSN 1050-4729
Keywords [en]
Human Detection, Robot Vision, Industrial Safety
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-30767DOI: 10.1109/ICRA.2013.6631419ISI: 000337617305131Scopus ID: 2-s2.0-84887269624ISBN: 978-1-4673-5641-1 (print)OAI: oai:DiVA.org:oru-30767DiVA, id: diva2:647365
Conference
2013 IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013
Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2018-01-11Bibliographically approved
In thesis
1. Vision-based Human Detection from Mobile Machinery in Industrial Environments
Open this publication in new window or tab >>Vision-based Human Detection from Mobile Machinery in Industrial Environments
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The problem addressed in this thesis is the detection, localisation and tracking of human workers from mobile industrial machinery using a customised vision system developed at Örebro University. Coined the RefleX Vision System, its hardware configuration and computer vision algorithms were specifically designed for real-world industrial scenarios where workers are required to wear protective high-visibility garments with retro-reflective markers. The demand for robust industry-purpose human sensing methods originates from the fact that many industrial environments represent work spaces that are shared between humans and mobile machinery. Typical examples of such environments include construction sites, surface and underground mines, storage yards and warehouses. Here, accidents involving mobile equipment and human workers frequently result in serious injuries and fatalities. Robust sensor-based detection of humans in the surrounding of mobile equipment is therefore an active research topic and represents a crucial requirement for safe vehicle operation and accident prevention in increasingly automated production sites. Addressing the described safety issue, this thesis presents a collection of papers which introduce, analyse and evaluate a novel vision-based method for detecting humans equipped with protective high-visibility garments in the neighbourhood of manned or unmanned industrial vehicles. The thesis provides a comprehensive discussion of the numerous aspects regarding the design of the hardware and the computer vision algorithms that constitute the vision system. An active nearinfrared camera setup that is customised for the robust perception of retroreflective markers builds the basis for the sensing method. Using its specific input, a set of computer vision and machine learning algorithms then perform extraction, analysis, classification and localisation of the observed reflective patterns, and eventually detection and tracking of workers with protective garments. Multiple real-world challenges, which existing methods frequently struggle to cope with, are discussed throughout the thesis, including varying ambient lighting conditions and human body pose variation. The presented work has been carried out with a strong focus on industrial applicability, and therefore includes an extensive experimental evaluation in a number of different real-world indoor and outdoor work environments.

Place, publisher, year, edition, pages
Örebro: Örebro university, 2016. p. 68
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 68
Keywords
Industrial Safety, Mobile Machinery, Human Detection, Computer Vision, Machine Learning, Infrared Vision, High-visibility Clothing, Reflective Markers
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-48324 (URN)978-91-7529-126-0 (ISBN)
Public defence
2016-04-14, Långhuset, Hörsal 1, Örebro universitet, Fakultetsgatan 1, Örebro, 10:15 (English)
Opponent
Supervisors
Available from: 2016-02-16 Created: 2016-02-16 Last updated: 2018-01-10Bibliographically approved

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Mosberger, RafaelAndreasson, Henrik

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