Innovative lighting and dynamic sound systems as well as adaptive object mapping solutions constitute a rapidly developing branch of lighting technology and multimedia technology. In order to make it possible to adjust the content to specific objects in the scene, it is necessary to correctly identify them and place them in the accepted frame of reference. Dynamic identification and tracking of objects can be carried out based on two particular types of input data: data from markers installed on objects and data from digital recording systems, founding the operation on infrared (IR), visible light (RGB) and the most advanced RGB-D (RGB and depth) analysis. Most systems used today are those that use various types of markers. This paper presents the advantages and disadvantages of such solutions as well as a target system for dynamic identification and mapping of objects and the human body based on the analysis of data from digital RGB-D cameras. Analyses of identification times, implementation of perspective transformations and 3D-to-2D transformations have been carried out in relation to a planar and cuboidal moving surface. Time analyses have been performed in relation to the resolution of registered and processed images.

The importance of reducing discomfort glare during the dynamic development of high luminance LEDs is growing fast. Smart control systems also offer great opportunities to reduce electricity consumption for lighting purposes. Currently, dynamic “intelligent” lighting systems are a rapidly developing field. These systems, consisting of cameras and lighting units, such as moving heads or multimedia projectors, are powerful tools that provide a lot of opportunities. The aim of this research is to demonstrate the possibilities of using the projection light in dynamic lighting systems that enable the reduction of discomfort glare and the light pollution phenomenon. The proposed system allows darkening or reducing the luminance of some sensitive zones, such as the eyes or the head, in real-time. This paper explores the development of the markerless object tracking system. The precise identification of the position and geometry of objects and the human figure is used for dynamic lighting and mapping with any graphic content. Time measurements for downloading the depth maps, as well as for identifying the human body’s position and pose, have been performed. The analyses of the image transformation times have been carried out in relation to the resolution of the images displayed by the projector. The total computation time related to object detection and image display translates directly into the precision of fitting the projection image to a moving object and has been shown.

For the proper operation of intelligent lighting, the precise detection of a human silhouette on the scene is necessary. Correctly adjusting the light beam divergence requires locating the detected figure in virtual three-dimensional coordinates in real time. The market is currently dominated by the markers systems. This paper is focused on the advanced solution of the markerless system of identifying and tracking characters based on deep learning methods. Analyses of the selected pose detection, holistic detection (including BalzePose and MoveNet models), and body segmentation (BlazePose and tfbodypix) algorithms are presented. The BlazePose model was implemented for both pose tracking and body segmentation in the markerless dynamic lighting and mapping system. This article presents the results of the accuracy analysis of matching the displayed content to a moving silhouette. An assessment of the illumination precision was done as the function of the movement speed for the system with and without delay compensation.

For 30 years the photometric descriptions of luminaires that are used in simulation, design and photometric calculations have not changed. For many years, technology did not allow for the detailing of digital photometric data of light sources and luminaires. In the second decade of the 21st century digital luminance cameras debuted on the markct., Theoretically these cameras offered the possibility of changing digital descriptions of luminaire parameters. Unfortunately, the huge amount of digital data associated with a single light source (even in excess of 1TB) significantly halted the development in this area. The author of this publication with his team developed a luminance camera, optimisation and verification algorithms which were used to propose the novel simplified luminance description of luminaires and light sources based on advanced methods of digital analysis of luminance data. This publication presents the result of the implementation of the presented method and compares it with the results from the popular lighting simulation calculation programs.