Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Abstract

Smart Cities refer to urban areas which exploits recent technologies for improving the performance, sustainability, and livability of their infrastructure and services. Crowd Density Analysis (CDA), a vital component of Smart Cities, involves the use of sensors, cameras, and data analytics to monitor and analyze the density and movement of people in public spaces. CDA utilizing DL harnesses the control of neural networks to mechanically and exactly evaluate the density of crowds in numerous settings, mainly in smart cities. DL techniques like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are trained on vast datasets of crowd videos or images to learn complex designs and features. These models can forecast crowd density levels, recognize crowd anomalies, and offer real-time visions into crowd behavior. This study designs an Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities (AICDA-SSC) technique. The aim of the AICDA-SSC method is to analyze the crowd density and classify it into multiple classes by the use of hyperparameter-tuned DL models. To accomplish this, the AICDA-SSC technique applies contrast enhancement using the CLAHE approach. Besides, the complex and intrinsic features can be derived by the use of the Inception v3 model and its hyperparameters can be chosen by the use of the marine predator's algorithm (MPA). For crowd density detection and classification, the AICDA-SSC technique applies a gated recurrent unit (GRU) model. Finally, a chaotic sooty tern optimizer algorithm (CSTOA) based hyperparameter selection procedure takes place to increase the effectiveness of the GRU system. The experimental evaluation of the AICDA-SSC technique takes place on a crowd-density image dataset. The experimentation values showcase the superior performance of the AICDA-SSC method to the recently developed DL models.