An integrated intelligent surveillance system for Industrial areas

This paper presents the design and implementation phases of a software prototype developed by the University of Parthenope for the SE4I project (Smart Energy Efficiency & Environment for Industry), funded by the”Progetti di ricerca industriale e lo Sviluppo sperimentale” (PNR 2015-2020). The prototype leverages advanced computer vision techniques based on deep learning architectures to address industrial security and monitoring needs. Specifically, the prototype tackles three key functionalities, (1) personnel and vehicle identification: The system recognizes authorized personnel and vehicle license plates within video streams captured in restricted industrial areas; (2) anomaly detection: The software can detect various anomalies in video feeds, including falls of personnel in monitored zones and unattended objects left in unauthorized areas; (3) smart parking management: The prototype identifies vacant parking spaces within camera-monitored zones, enabling efficient parking management. These functionalities are integrated into the software prototype, and its performance has been thoroughly evaluated.