AI Activity Tracker Office

AI Activity Tracker Office

Mr. Ravindra S. Govind, Prit Patel, Het Chopda, Krish Patel, Dhruvkumar Vasani

1. Mr. Ravindra S. Govind, Lecture, Computer Engineering, Mahavir Polytechnic, Nashik

2. Mr. Prit Patel, Student, Computer Engineering, Mahavir Polytechnic, Nashik

3. Mr. Het Chopda, Student, Computer Engineering, Mahavir Polytechnic, Nashik

4. Mr. Krish Patel, Student, Computer Engineering, Mahavir Polytechnic, Nashik

5. Mr. Dhruvkumar Vasani, Student, Computer Engineering, Mahavir Polytechnic, Nashik

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Abstract - This paper presents a real-time work monitoring system using YOLOv8 and OpenCV for detecting and tracking individuals in a workspace. The system identifies people and objects such as desks, laptops, and computers to determine worker activity status. Optical flow-based video stabilization is implemented to enhance detection accuracy. The model processes live video feeds, identifies workers, and tracks their proximity to desks and devices. If a worker is away from their workstation for an extended period, the system records the absence in a log file. The proposed approach combines object detection with time-based tracking, enabling automated attendance monitoring and workplace productivity assessment. The system is designed to provide real-time insights for employers and researchers studying workspace efficiency. Results demonstrate its effectiveness in tracking personnel movement and detecting absence duration with reasonable accuracy.

Keywords: work monitoring, YOLOv8, object detection, attendance tracking, video stabilization, real-time surveillance.