Dynamic Face Tracking and Route Prediction Across Distributed Camera Network Using MobileFaceNet and Partial Trajectory Matching

In an era where security and surveillance are paramount, this paper presents a comprehensive framework for real-time face recognition and pedestrian trajectory prediction, leveraging the capabilities of MobileFaceNet and a Partial Trajectory Matching Algorithm. The system is designed to enhance surveillance and monitoring by integrating accurate face recognition with predictive path analysis. Known face images and historical trajectory data are utilized to recognize individuals and forecast their movement patterns. The framework processes live video feeds, detecting and identifying faces in real time. Upon identification, it segments and matches the pedestrian’s trajectory against historical data to predict future movements. Performance is evaluated using metrics such as precision, recall, and F1 score. The results demonstrate the efficacy of the proposed approach in improving situational awareness and response times in security applications.