Face Recognition in Smart Cameras by Yolo8

Abstract

Smart AI Cameras have become a vital tool for enhancing security in several industries, such as industrial, transportation, and retail. This study investigates the methods that might be used to recognize moving objects in both daytime and nighttime settings. In this paper, convolutional neural networks, and recurrent neural networks—two deep learning techniques for object recognition—are investigated. We look at datasets containing a range of objects, lighting conFigureurations, and camera angles to determine how well these algorithms perform. In our research, we compared results from two separate datasets using YOLOv8. After all, we compared our methods and results with other scientists' research. We discussed the importance of camera placement, lighting issues, and algorithm choice for effective object detection. We evaluate the cameras' ability to recognize and follow moving things, as well as how well they can communicate with other security systems like alarms and access control. Our research demonstrates that smart AI cameras may significantly improve security in a variety of situations and that choosing the right algorithm and placing the camera is crucial for maximizing their effectiveness. For enterprises considering the usage of smart AI cameras for security, our research offers helpful information.