Real Time Surveillance System Using Yolov8

This research study presents a novel real-time surveillance system with object detection and email alerting capabilities. The proposed system utilizes the YOLOv8 object detection model for real-time object detection, with a focus on detecting humans. The system architecture consists of a Python backend, which uses Flask framework for web interfacing and the OpenCV library for capturing and processing the videos. The YOLOv8 model can accurately identify a large variety of objects and it is used for object detection. The system also incorporates email functionality using the smtplib library to send email alerts with detected human images as attachments. Overall, the proposed system achieves high accuracy in detecting humans and effectively sends email alerts in real-time. The major advantage of the proposed model is that the users may quickly configure the system and get notifications at their preferred email address with the developed user-friendly online interface.