Intruder Alert System

Abstract

Abstract: In this paper, we present an intruder alert system that detects and classifies potential intruders in a given environment using computer vision and machine learning. A network of cameras and a central processing unit run an object detection algorithm and a machine learning classifier in the system. This project was designed and built with an Arduino mega microcontroller, system connected to a GSM module with SIM (subscriber identification module) from a network provider (LDR) sensor which is used in this project is connected to a microcontroller unit, GSM module, and other electronic devices which thus detect any intruder in a classified area, activates the alarm system, and sends text message notifications to the house owner specifying the location and time of intrusion to the house owner's programmed phone number. The design worked in real-time in an area with good network coverage, as expected, according to preliminary testing. It has a wide range of applications in surveillance, particularly in industrial and home security, where internet connectivity is unavailable. The findings demonstrate that the system is capable of accurately identifying and categorizing possible intruders with low rates of false positives.