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@article{187489,
        author = {Safia Alam and sonika Duvvuru and Sanjay MP and Vinay Hulamani and N.UmaMaheshwari},
        title = {Lost Item Detection and Tracking System Using YOLOv8 and DeepSORT for Intelligent Surveillance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5462-5467},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187489},
        abstract = {Lost and unattended items are common in public environments such as campuses, malls, and transportation hubs. Traditional CCTV surveillance requires constant manual monitoring, which is time-consuming and prone to human error. This project proposes an AI-based Lost Item Detection and Tracking System using YOLOv8 for real-time object detection and DeepSORT for multi-object tracking. The system identifies personal belongings such as bags and phones, associates them with the nearest detected person, and monitors separation behaviour. If an item remains unattended beyond a specified duration, the system triggers an automated alert and logs the event through a Flask-based dashboard. Experimental testing on recorded and live CCTV feeds demonstrates reliable detection accuracy and stable ID tracking, confirming the system’s suitability for intelligent surveillance applications.},
        keywords = {YOLOv8, DeepSORT, Object Tracking, Lost Item Detection, Smart Surveillance, Computer Vision.},
        month = {November},
        }