AI-based Pilgrim Detection using Convolutional Neural Networks
This work addresses safety and security for pilgrims in Islamic holy sites, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of monitoring pilgrims in crowded holy sites by proposing an AI-based detection system using convolutional neural networks, achieving a mean average precision of 51% with Faster RCNN and Inception v2.
Pilgrimage represents the most important Islamic religious gathering in the world where millions of pilgrims visit the holy places of Makkah and Madinah to perform their rituals. The safety and security of pilgrims is the highest priority for the authorities. In Makkah, 5000 cameras are spread around the holy for monitoring pilgrims, but it is almost impossible to track all events by humans considering the huge number of images collected every second. To address this issue, we propose to use artificial intelligence technique based on deep learning and convolution neural networks to detect and identify Pilgrims and their features. For this purpose, we built a comprehensive dataset for the detection of pilgrims and their genders. Then, we develop two convolutional neural networks based on YOLOv3 and Faster-RCNN for the detection of Pilgrims. Experiments results show that Faster RCNN with Inception v2 feature extractor provides the best mean average precision over all classes of 51%.