Intruder Detection System Using Deep Learning

Abstract

A higher standard of living is primarily dependent on the safety of people and their possessions. Much progress has been made in the areas of making homes automatic and more secured. The home environment has seen increased remote monitoring, home security, and appliance control because to technological advancements and the (IoT). In order to track activity inside the user's house and offer a report, a number of home automation systems have been developed. Modern home automation systems include cameras and motion detectors for home security. However, one of the biggest challenges still lies in the logical component of avoiding bogus or superfluous messages. Smart home automation is effective because of intelligent monitoring and response.Utilizing a deep learning model, a technique is developed to enhance the smart home automation system intruder detection and reduce the likelihood of false alarm. Based on his walk, a person seen on video is either categorized as an intruder or a resident of the home. A PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, an ESP32 security camera, and the recommended method's prototype were all used in the construction of the system. A CNN model experiment using human motion patterns was carried out to assess the classification for the identification of people.