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Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj

Roman Bhuiyan¹, Junaidi Abdullah¹, Noramiza Hashim¹

¹Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

A novel deep Hajj crowd dilated convolutional neural network (DHCDCNNet) effectively analyzes crowd density during the Hajj pilgrimage. This deep learning approach improves safety by accurately estimating crowd numbers in real-time surveillance scenarios.

Keywords:

FCNN Hajj-Crowd dataset crowd density classification deep augmentation morphological operation

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Area of Science:

Computer Vision
Artificial Intelligence
Crowd Dynamics

Background:

The annual Hajj pilgrimage in Mecca attracts millions of Muslims globally.
Managing massive crowds poses significant safety and logistical challenges.
Accurate crowd density estimation is crucial for preventing accidents and ensuring pilgrim safety.

Purpose of the Study:

To introduce a deep learning model for accurate crowd density analysis in the Hajj pilgrimage context.
To develop an effective data augmentation technique for Hajj-specific crowd scenarios.
To provide a robust solution for crowd density measurement using distant surveillance cameras.

Main Methods:

Developed a deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis.
Implemented a data augmentation technique with two routes: magnitude/polar magnitude and morphological/skeleton transformation.
Utilized a single framework for extracting both high-level and low-level features for comprehensive analysis.

Main Results:

The DHCDCNNet achieved high accuracy across multiple datasets: 97% (JHU-CROWD), 89% (UCSD), and 100% (Hajj-Crowd).
VGGNet and ResNet50 approaches also demonstrated strong performance on the Hajj-Crowd dataset (98% and 99% accuracy, respectively).
The model effectively handles high-density scenarios (7-8 persons/sqm) common in the Tawaf area.

Conclusions:

The proposed DHCDCNNet offers a highly accurate and reliable method for crowd density analysis during the Hajj.
The augmentation technique successfully generated a valuable dataset for Hajj pilgrimage scenarios.
This research contributes to enhanced crowd management strategies for large-scale religious gatherings.