Real-Time Intelligent Video Surveillance System using Recurrent Neural Network
Pooja Br, N. Rajkumar
Abstract
Security is a significant concern at all locations where CCTV cameras are installed. Security is a top priority; you must invest considerable time and effort to keep track of everything. Shortly, developments in computer vision may substantially impact video surveillance systems. To measure the video feed in real-time and detect any abnormal behavior without human intervention., such as violence or theft. The real-time video was captured as data with regular and strange events, and it has been trained (21 videos) and tested (16 videos) using a deep learning neural network. The captured video has been converted as frames using a Spatio-temporal autoencoder and 3D convolution network with the help of the RNN algorithm with a threshold value of 0.006 to detect the events. The RNN algorithm analyzed the captured video to see the possibilities with higher accuracy, 96%, than others. A data processing model for event detection will be constructed using deep-learning neural network technologies. A sophisticated monitoring system also transmits real-time video and voice communications to the web.