Computer Vision (CV) and Machine Learning (ML) have transformed manufacturing by enabling real-time monitoring and optimization. This study introduces a novel CV-based system employing multiple RGB 2D cameras for the localization of human workers on the shop floor. The system utilizes the SCRFD pre-trained 2D person detection neural network, leveraging existing surveillance and common video cameras to monitor worker positions accurately. By tracking workers in real time, the system enhances safety by detecting hazardous situations, thereby preventing accidents. The proposed methodology was validated using videos from an industrial setting in the production of wooden house modules, demonstrating robust performance with a detection rate of 67.37% and a mean absolute error of 0.5 m. This approach provides a cost-effective and precise solution to improve worker safety and operational efficiency in manufacturing environments, advancing the integration of advanced CV techniques in industry.