Abstract:
Bahria University, until now, has used primitive methods for attendance, such as; roll calls and paper signatures. Attendance tracking might sound like a simple and mundane task, but it is plagued by many errors which are surprisingly tricky to get right with the traditional methods being used. The idea of an Intelligent Object Tracking System with a Pan-Tilt camera for the University is presented in this thesis. Instead of physical sign sheets or simple biometric scanners, the projected system allows real-time facial recognition that is non-intrusive and unbiased to external conflicts. The system combines MTCCN face detection with ResNet embeddings with two separate user interfaces, making it a dual-interface architecture. A supreme administrative control is provided by a Streamlit dashboard while monitoring is influenced with the help of a Flask server. Live video feeds can be tracked and monitored by the instructors over local networks without the need of special equipment or the need to run back to administrative work stations. A PostgreSQL database makes it easy to manage the attendance records and support any needed analysis. The system hit a 97.2% of accuracy during its trial testing.
