Deep Learning for Real-Time Video Analytics in Smart Cities: Enhancing Traffic and Crowd Management

Abstract

This paper explores the application of deep learning models in real-time video analytics for smart city applications, with a particular focus on enhancing traffic management, public safety, and crowd control. As urban populations grow and cities become more complex, the need for efficient, scalable, and intelligent solutions for managing urban environments is critical. Deep learning techniques, particularly those involving convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer powerful tools for processing and analyzing vast streams of real-time video data. These models can identify patterns, predict trends, and make autonomous decisions, enabling smart cities to optimize traffic flow, reduce congestion, and improve public safety by monitoring and analyzing crowd behaviors. This paper provides a detailed examination of the potential applications of deep learning for smart city video analytics, the current challenges, and the future directions of this rapidly evolving field.