Smart Video Detection: Deep Learning for Enhanced Home Security

Home video recognition autonomous systems based on traditional approach for face image analysis are inherently unable to provide the required real time/intelligent analytics to adapt to contemporary security threats. This project is centered around utilizing smart technology to bolster home security, more specifically with the help of smart video surveillance. This is the system that learns to recognize the faces and objects in home video to alert homeowners when an intruder enters, but without compromising the privacy of its residents. This has initiated a truly human revolution - introducing deep learning algorithms to enable machines to identify intricate patterns from optical data streams. The purpose of this paper is to provide an insight into the disruptive value of deep learning based algorithms including CNN, ResNet50, VGG16 in the field of home security, exposing the limitations of traditional face image analysis techniques and explaining the revolutionary power of new machine learning approaches. Model accuracy in Normal CNN almost up to 99 percent VGG16 Algorithm too up to 95 percent. This vision recognition system is a substantial leap in improvement in security and surveillance.