An autonomous system for identifying and tracking characters using neural networks

Abstract

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.