Manuscript received October 20, 2022; revised November 16, 2022, accepted November 30, 2022.

Abstract—During the past decade, artificial intelligence technologies, especially Computer Vision (CV) technologies, have experienced significant breakthroughs due to the development of deep learning models, particularly Convolutional Neural Networks (CNNs). These networks have been utilized in various research applications, including astronomy, marine sciences, security, medicine, and pathology. In this paper, we build a framework utilizing CV technology to support decision-makers during the Hajj season. We collect and process real-time/instant images from multiple aircraft/drones, which follow the pilgrims while they move around the holy sites during Hajj. These images, taken by multiple drones, are processed in two stages. First, we purify the images collected from multiple drones and stitch them, producing one image that captures the whole holy site. Second, the stitched image is processed using a CNN to provide two pieces of information: (1) the number of buses and ambulances; and (2) the estimated count of pilgrims. This information could help decision-makers identify needs for further support during Hajj, such as logistics services, security personnel, and/or ambulances.

Keywords—unmanned aerial vehicles, deep networks, instant crowd counting, vehicle detection, image stitching, Hajj

Cite: Abdullah M. Algamdi and Hammam M. Alghamdi, "Instant Counting & Vehicle Detection during Hajj Using Drones," Journal of Image and Graphics, Vol. 11, No. 2, pp. 204-211, June 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.