Deep Learning Techniques for Real-Time Video Processing and Analysis

Abstract

Real-time video processing and analysis have become critical components in various applications, including security surveillance, autonomous vehicles, healthcare, and entertainment. Deep learning techniques, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have significantly improved the performance of video processing systems. This article explores the role of deep learning in real-time video analysis, focusing on applications such as object detection, motion tracking, event recognition, and video classification. We examine the challenges involved in processing large video data in real-time and the potential solutions deep learning offers to address these challenges.