A Yolo-based object monitoring approach for smart shops surveillance system

Object monitoring in video analysis is a crucial component of smart surveillance systems, enabling the observation and analysis of customer behavior and interactions with various items. This research aims to present a vision-based approach to object monitoring specifically tailored for surveillance systems in smart shops, utilizing an intelligent Internet of Things device. The focus of this study revolves around monitoring jewelry objects within jewelry shops. To accomplish the object monitoring task, an object detector based on Yolo architecture is employed, allowing for the detection and localization of objects. The Yolo network is trained and validated using a customized dataset that encompasses two distinct classes of jewelry objects: rings and earrings. The experimental findings show that the suggested object monitoring approach fulfills the real-time requirements for monitoring objects within smart shops' surveillance systems.