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A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-2953-1564
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-0217-9326
2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 10, 17952-17980 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions.

Place, publisher, year, edition, pages
2014. Vol. 14, no 10, 17952-17980 p.
Keyword [en]
infrared vision, human detection, industrial safety, high-visibility clothing
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-39811DOI: 10.3390/s141017952ISI: 000344455700006PubMedID: 25264956Scopus ID: 2-s2.0-84908518665OAI: oai:DiVA.org:oru-39811DiVA: diva2:772165
Available from: 2014-12-16 Created: 2014-12-16 Last updated: 2017-10-17Bibliographically 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: 2017-10-17Bibliographically approved

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