<![CDATA[In modern industrial environments, effective supervision of worker attendance and activity is essential to ensure safety and optimize productivity. Inadequate monitoring can lead to increased workplace accidents and reduced operational efficiency. To address these challenges, we propose an intelligent monitoring system leveraging computer vision techniques, implemented using PyTorch and the YOLO (You Only Look Once) object detection framework. The system utilizes video input from CCTV or cameras installed in the workspace and performs real-time analysis through several key modules: (i) Worker Counting, which detects and counts the number of employees present; (ii) Activity Detection, which classifies workers as actively working or idle (e.g., sleeping or using mobile phones); (iii) Fall Detection, which identifies and alerts the server in case of worker falls; and (iv) Absent Detection, which tracks and flags workers who leave the monitored area. Detection results and relevant video segments are automatically archived in local storage, enabling retrospective review and continuous monitoring. By integrating these modules, the proposed system aims to enhance workplace safety, support real-time remote supervision, and improve overall operational effectiveness in industrial settings.]]>
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