Panoramic Video Surveillance: An Analysis of Burglary Detection Based on YOLO Framework in Residential Areas

Abstract

Artificial Intelligence (AI) is a technique that incorporates human intelligence into mundane activities. And there is no question that AI is significantly affecting security and surveillance. Although relying on numerous resources, finding answers, and implementing technology for decades, our security and surveillance systems still have flaws. In every country around the globe, the use of video security and surveillance is becoming more widespread. Nonetheless, a wide range of businesses has made use of it, including hospitals, universities, and warehouses. Yet, as people are limited in their ability to vigilantly monitor live video streams, deep learning was developed to better fill the position. Unfortunately, there are other problems with images in the real world, including jitter or blurring caused by rotating objects, noise, and sharpness concerns. As a result, deep learning technology for surveillance has considerably improved in recent years. The main objective of this study is to detect burglars using deep learning technology. This system aims to take video surveillance of the residential environment as input and pass it into the Yolo model to increase the speed and accuracy of the system to detect burglars in the residential. This system mainly concentrates on object detection.