A Hajj And Umrah Location Classification System For Video Crowded Scenes

TL;DR

This paper introduces an automatic system for classifying Hajj and Umrah ritual locations in videos, utilizing a new dataset and machine learning classifiers, achieving over 90% accuracy in recognizing six ritual types.

Contribution

The paper presents a novel system with a new dataset for classifying Hajj and Umrah locations, improving accuracy over previous methods.

Findings

Achieved over 90% accuracy in location classification

Developed the HUER dataset for ritual scenes

Enhanced state-of-the-art performance in ritual scene recognition

Abstract

In this paper, a new automatic system for classifying ritual locations in diverse Hajj and Umrah video scenes is investigated. This challenging subject has mostly been ignored in the past due to several problems one of which is the lack of realistic annotated video datasets. HUER Dataset is defined to model six different Hajj and Umrah ritual locations[26]. The proposed Hajj and Umrah ritual location classifying system consists of four main phases: Preprocessing, segmentation, feature extraction, and location classification phases. The shot boundary detection and background/foregroud segmentation algorithms are applied to prepare the input video scenes into the KNN, ANN, and SVM classifiers. The system improves the state of art results on Hajj and Umrah location classifications, and successfully recognizes the six Hajj rituals with more than 90% accuracy. The various demonstrated experiments show the promising results.