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Estimating the 3D Position of Humans Wearing a Reflective Vest Using a Single Camera System
Örebro University, School of Science and Technology, Örebro University, Sweden. (MRO)
Örebro University, School of Science and Technology, Örebro University, Sweden. (MRO)ORCID iD: 0000-0002-2953-1564
2014 (English)In: Field and Service Robotics: Results of the 8th International Conference / [ed] Yoshida, Kazuya, Tadokoro, Satoshi, Springer Berlin/Heidelberg, 2014, 143-157 p.Chapter in book (Refereed)
Abstract [en]

This chapter presents a novel possible solution for people detection and estimation of their 3D position in challenging shared environments. Addressing safety critical applications in industrial environments, we make the basic assumption that people wear reflective vests. In order to detect these vests and to discriminate them from other reflective material, we propose an approach based on a single camera equipped with an IR flash. The camera acquires pairs of images, one with and one without IR flash, in short succession. The images forming a pair are then related to each other through feature tracking, which allows to discard features for which the relative intensity difference is small and which are thus not believed to belong to a reflective vest. Next, the local neighbourhood of the remaining features is further analysed. First, a Random Forest classifier is used to discriminate between features caused by a reflective vest and features caused by some other reflective materials. Second, the distance between the camera and the vest features is estimated using a Random Forest regressor. The proposed system was evaluated in one indoor and two challenging outdoor scenarios. Our results indicate very good classification performance and remarkably accurate distance estimation especially in combination with the SURF descriptor, even under direct exposure to sunlight.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2014. 143-157 p.
Series
Springer Tracts in Advanced Robotics, ISSN 1610-7438 ; 92
Keyword [en]
People Detection, Industrial Safety, Reflective Vest Detection
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-41334DOI: 10.1007/978-3-642-40686-7_10ISBN: 978-3-642-40685-0 (print)ISBN: 978-3-642-40686-7 (print)OAI: oai:DiVA.org:oru-41334DiVA: diva2:780355
Projects
SAVIE
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2016-03-23Bibliographically 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. 68 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 68
Keyword
Industrial Safety, Mobile Machinery, Human Detection, Computer Vision, Machine Learning, Infrared Vision, High-visibility Clothing, Reflective Markers
National Category
Computer Science
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: 2016-03-24Bibliographically approved

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