A Comparison of ML and DL Approaches for Crowd Analysis on the Hajj Pilgrimage

Abstract

In proportion to the growth in human population, there has been a substantial rise in the number of crowds in public places. The more crowded a place, the more risk of stampedes. Therefore, crowd management is very critical to ensure the safety of the crowds. Crowd monitoring is an effective approach to monitor, control and understand the behavior of the density of the crowd. One of the efficient automated video monitoring techniques to ensure public safety is crowd density estimation. Crowd density analysis is used primarily in public areas that are usually crowded with people such as stadiums, parks, shopping malls and railway stations. In this research, crowd density analysis by machine learning is presented. The main purpose of this model is to determine the best machine learning algorithm with the highest performance for crowd density classification. This model is focusing on machine learning algorithms such as traditional machine learning algorithms and deep learning algorithms. For traditional machine learning algorithms, Histogram Oriented Gradients (HOG) and Local Binary Pattern (LBP) have been used to extract important features from the input crowd images before being fed into Support Vector Machine (SVM) for classification. For deep learning algorithms, custom Convolutional Neural Network (CNN) together with two famous CNN architectures named Residual Network (ResNet) and Visual Geometry Group Network (VGGNet) were implemented as other methods in this paper for comparison. Other than that, the performance evaluation of the algorithm was measured based on the accuracy of the models. The performance of all different models was recorded and compared.