Deep neural network-based detection of pilgrims location in Holy Makkah

Taha M. A., Direkoglu M. S., Direkoglu C.

International Journal of Communication Systems, vol.35, no.16, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 16
  • Publication Date: 2022
  • Doi Number: 10.1002/dac.4792
  • Journal Name: International Journal of Communication Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: classification, feature extraction, location recognition, pilgrim, preprocessing, segmentation, CLASSIFICATION
  • Middle East Technical University Affiliated: Yes


© 2021 John Wiley & Sons Ltd.Consistently almost 2,000,000 Islamic worshipers from everywhere throughout the world are going to Makkah to accomplish the Hajj. With the inconsistent expansion of the pilgrims count, the believers from various parts of the world think that it is troublesome in recognizing their position and might be directed wrongly because of barriers in communication. The researchers have found a way to help and pursue every pilgrim in recognizing their position. But they were confronted with challenges due the research barriers. While various investigations have already been initiated to realize viable and proficient monitoring system in Makkah, but most of the works have been neglected. Additionally, no studies have been proposed to recognize the exact areas of worshipers. This paper develops a solution to distinguish hotspot territories in Makkah by utilizing a novel digital image processing technique. As convolutional neural network (CNN) has found to be a successful method found in most of the image processing methods, it is applied in this paper in addition to geographical information of the images. To show the consequences of this network, a MATLAB simulation-based demonstration system is utilized.