The article explores the application of stereo images and neural networks for tracking designated manufacturing employees, with a focus on optimizing production processes. The primary objective of this study is to present a novel approach for the automatic construction of movement trajectories of employees, an issue of significant importance for improving production efficiency. By analyzing these trajectories, valuable insights can be gained regarding the optimal arrangement of workstations, facilitating adjustments to their positions in alignment with the actual workflows. This approach aligns closely with principles of lean manufacturing, offering a method to enhance operational efficiency. The authors propose a solution based on U-Net type neural networks for classifying objects within stereo images, alongside the integration of stereoscopic imaging with regression techniques for accurate 3D localization of objects. A thorough analysis of the proposed AI models is presented, accompanied by the results of practical tests conducted under varying configuration parameters of the image processing system. The study highlights the novelty of the approach, contributing to the advancement of automated monitoring and optimization in manufacturing environments.