Login

AI-based Pilgrim Detection using Convolutional Neural Networks
Ref: CISTER-TR-200109       Publication Date: 2 to 5, Sep, 2020

AI-based Pilgrim Detection using Convolutional Neural Networks

Ref: CISTER-TR-200109       Publication Date: 2 to 5, Sep, 2020

Abstract:
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%.

Authors:
Marwa Ben Jabra
,
Adel Ammar
,
Anis Koubâa
,
Omar Cheikhrouhou
,
Habib Hamam


Events:

ATSIP 2020
2, Sep, 2020 >> 5, Sep, 2020
5th International Conference on Advanced Technologies for Signal and Image Processing
Sfax, Tunisa


5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP 2020).
Sfax, Tunisa.



Record Date: 14, Jan, 2020