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@article{205228,
        author = {Ajinkya Jagan Naik and Chaitanya Suresh Utekar and Sahil Lukade and Prathamesh Bhatpure},
        title = {Human Congestion Monitoring System Using YOLOv8, Deep SORT, and Re-Identification for Real-Time Crowd Analysis and Missing Person Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6083-6088},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205228},
        abstract = {Managing large crowds in public spaces such as railway stations, malls, and public events poses significant safety and security challenges. Overcrowding, sudden surges, and the need to locate missing individuals demand automated, real-time solutions that go beyond conventional surveillance. This paper presents a Human Congestion Monitoring System that integrates YOLOv8-based person detection, Deep SORT multi-object tracking, ResNet-50 Re-Identification (ReID), and FAISS-based vector search to enable live crowd counting, density thresholding, surge detection, and missing person tracking through a unified web-based dashboard. The backend is built with FastAPI and communicates with the frontend in real time via Web Sockets. Alerts for crowd threshold breaches, sudden surges, and missing person matches are generated automatically and persisted in a SQLite database. Experimental results on live webcam streams and uploaded video files confirm the system’s effectiveness, achieving accurate person counts, responsive alerting, and reliable missing person re-identification across multiple video sources.},
        keywords = {Human Congestion Monitoring, YOLOv8, Deep SORT, Re-Identification (ReID), FAISS, FastAPI, Web-Socket, Missing Person Detection, Crowd Density, Real-Time Surveillance},
        month = {June},
        }