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, 2020Abstract:
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%.
Marwa Ben Jabra
Anis KoubâaEvents:
Document:
View
DownloadAdditional Files:
5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP 2020), pp 1-6.
Sousse, Tunisa.
DOI:10.1109/ATSIP49331.2020.9231549.
ISBN: 978-1-7281-7513-3.
ISSN: 2687-878X.
Record Date: 14, Jan, 2020