Being inconspicuous in the dark outdoors can cause accidents including physical injuries. To prevent pedestrian being involved in accidents it is necessary to make them most visible to approaching people.This Master’s Thesis examines the use of retroreflective clothing in a semi-dark urban environment to provide safety through conspicuity. Through analysing the lighting situation in Stockholm, the ability of the human vision, reactions and existing products it has led to experiments and surveys to find the most efficient line placement and pattern to make an individual recognizable as human on approach.The results were that body outlines and horizontal lines along joints made a human most identifiable. Most conspicuity was given when lines were wider than 2cm and patterns contrasted to the surroundings.As the experiment was conducted in a semi-dark setting, different retroreflective design solutions might be more adequate for other lighting scenarios with more or less light

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.