Hajj and Umrah Event Recognition Datasets

TL;DR

This paper introduces the HUER datasets, a comprehensive collection of videos and images capturing various Hajj and Umrah rituals and actions, aimed at advancing event recognition research in this religious context.

Contribution

It presents the first extensive datasets covering six ritual events and nine human actions during Hajj and Umrah, with detailed video and image data for recognition tasks.

Findings

Datasets include 1280x720 images and 640x480 videos.

Videos average 20 seconds at 30 fps.

First dataset covering multiple Hajj and Umrah events.

Abstract

In this note, new Hajj and Umrah Event Recognition datasets (HUER) are presented. The demonstrated datasets are based on videos and images taken during 2011-2012 Hajj and Umrah seasons. HUER is the first collection of datasets covering the six types of Hajj and Umrah ritual events (rotating in Tawaf around Kabaa, performing Sa'y between Safa and Marwa, standing on the mount of Arafat, staying overnight in Muzdalifah, staying two or three days in Mina, and throwing Jamarat). The HUER datasets also contain video and image databases for nine types of human actions during Hajj and Umrah (walking, drinking from Zamzam water, sleeping, smiling, eating, praying, sitting, shaving hairs and ablutions, reading the holy Quran and making duaa). The spatial resolutions are 1280 x 720 pixels for images and 640 x 480 pixels for videos and have lengths of 20 seconds in average with 30 frame per second rates.