Analyzing Surveillance Videos in Real-Time using AI-Powered Deep Learning Techniques

Abstract

Modern surveillance systems are going to revolutionize the whole world as they make it possible to analyze, track and judge the activities in a specific area or context. And all this becomes possible just with the help of Real-time video processing and by advanced machine learning. This article will lead you to the developmental approaches and the previous studies of surveillance systems how they work and what are the bases of it? We follow the stream from the analog system to the advanced artificial intelligence systems and try to cover every instance in the progress of AI. Video capture, pre-processing, feature extraction, object recognition, tracking, and behavior analysis are the most important factors which we mostly cover in this article. Recently achieved advancements in artificial intelligence are contributing to the precision of surveillance systems, including deep learning models, edge computing, and hardware acceleration. In this article we discuss the surveillance system installed in a public park for security purposes based on neural networks (RNNs) for behavior analysis and convolutional neural networks (CNNs) for object recognition, to illustrate real-world situations. This system gained 95% accuracy which enhances the working that this system can precisely predict suspicious activities under the area it covers.