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@article{201961,
        author = {Shraddha Kadam and Prof. Oblisamy L and Seema  Madurkar and Nikita Shendage and Rutika Ghaste},
        title = {Real-Time Passenger Tracking System in an Airport Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5732-5737},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201961},
        abstract = {Airports are highly dense areas that necessitate efficient passenger monitoring not only to ensure their security but also the efficient functioning of the airport itself. The current monitoring techniques are mainly based on sensing technologies, including RFID, Wi-Fi, and Bluetooth Low Energy. Although these techniques can provide the capability to perform extensive monitoring, they do not have the means to recognize passengers individually and require costly infrastructure installations, which can offer very little identification information. The presented paper discusses the design and implementation of the real-time passenger monitoring system that incorporates the facial recognition techniques implemented by deep learning techniques into the zone-based monitoring approach. To extract precise features, the DeepFace system with FaceNet embeddings was chosen as the main component of this work, whereas OpenCV library was chosen to detect faces in real-time video streams. Moreover, the number of people present in a certain zone will be determined in real-time as well. According to the experimental findings, the average recognition rate of proposed model equals 92% with a processing time of 100-150 ms per frame. In comparison with the Haar Cascade algorithm with LBPH, the presented model proves to be better in terms of cost-efficiency, recognition rate, and robustness.},
        keywords = {Facial Recognition, Passenger Tracking, Deep Learning, Computer Vision, Real-Time Surveillance, Smart Airports, Zone-Based Tracking},
        month = {May},
        }