Identification of Locations in Mecca using Image Pre-Processing, Neural Networks and Deep Learning

Every year, more than two million Muslim pilgrims from all over the world visit Mecca to perform Hajj worship. The challenge of location identification in dense crowds is significant, leading to dangerous consequences such as injury or loss. Existing works for person localization remain challenged, especially in crowded places like Mecca during Hajj. In this work, we propose a novel location identification method using image pre-processing and different machine learning classifiers, with the creation of a new image dataset for hotspot locations in Mecca. Image pre-processing algorithms are applied to enhance the geographic information present in the images, and the obtained features are classified using CNN, ANN, and SVM classifiers. Extensive evaluations reveal that the proposed pre-processing algorithm with CNN achieves the best localization performance with an accuracy of 90%, followed by ANN with an accuracy of 84%, and SVM with an accuracy of 80.50%. Without pre-processing, the accuracies are significantly lower: 63% for CNN, 73% for ANN, and 71.50% for SVM. In addition, the proposed approach was compared with other deep learning models, VGG16, AlexNet and ResNet50 on our dataset achieving an accuracy of 61%, 65% and 62%, respectively. Results demonstrate the effectiveness of our proposed method in comparison with other deep transfer learning methods on a small dataset, offering promising solutions for crowded place navigation.