Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.
AI and Big Data Department, Endicott College, Woosong University, Daejeon 300-718, Korea.
Sensors (Basel). 2022 Jul 7;22(14):5102. doi: 10.3390/s22145102.
Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.
每年都有将近 200 万来自世界各地的穆斯林朝圣者到麦加进行朝觐。每年，朝圣者的数量都在增加，这引发了人们对如何处理如此庞大的人群以及避免不愉快的事故或拥挤灾难的担忧。在本文中，我们提出了用于人群密度分析的深度学习朝觐人群扩张卷积神经网络（DHCDCNNet）。该研究还提出了一种基于朝觐场景的扩充技术来创建额外的数据集。我们利用单个框架来提取高层和低层特征。为了创建额外的数据集，我们将图像增强的过程分为两条路线。在第一条路线中，我们利用幅度提取，然后是极幅度。在第二条路线中，我们执行形态学操作，然后将图像转换为骨架。本文提出了一种使用指向远方的监控摄像机测量人群密度的解决方案。所提出的方法包括一种基于 FCNN 的人群分析技术，特别是用于分类人群密度。当有大量的朝圣者在 tawaf 区域周围移动时，视频分析存在几个障碍，密度在每平方米 7 到 8 人之间。所提出的 DHCDCNNet 方法在 JHU-CROWD 数据集、UCSD 数据集和提出的 Hajj-Crowd 数据集上的准确率分别为 97%、89%和 100%。使用 VGGNet 方法，所提出的 Hajj-Crowd 数据集、UCSD 数据集和 JHU-CROW 数据集的准确率分别为 98%、97%和 97%。使用 ResNet50 方法，所提出的 Hajj-Crowd 数据集、UCSD 数据集和 JHU-CROW 数据集的准确率分别为 99%、91%和 97%。
