Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

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.