Hajj and Umrah Event Recognition Datasets (HUER)

Hajj and Umrah Event Recognition Datasets (HUER) 是由乌姆·阿尔-库拉大学朝觐与小朝卓越研究中心创建的，专注于识别朝觐和小朝仪式中的各种人类活动。数据集包含视频和图像，记录了2011-2012年朝觐和小朝季节的活动，涵盖了六种朝觐和小朝仪式事件以及九种人类行为。数据集的视频和图像分辨率分别为640x480像素和1280x720像素，平均长度为20秒，每秒30帧。HUER旨在通过识别和分类这些活动，解决朝觐和小朝期间的安全和管理问题，如检测异常行为、人群拥挤等。

Hajj and Umrah Event Recognition Datasets (HUER) was developed by the Centre of Excellence for Hajj and Umrah Research at Umm Al-Qura University, targeting the recognition of diverse human activities during Hajj and Umrah rituals. The dataset consists of videos and images that document activities during the 2011–2012 Hajj and Umrah seasons, covering six categories of Hajj and Umrah ritual events and nine types of human behaviors. The resolutions of the videos and images are 640×480 pixels and 1280×720 pixels respectively, with an average clip duration of 20 seconds and a frame rate of 30 frames per second. The HUER dataset is designed to address safety and management challenges during Hajj and Umrah, including the detection of abnormal behaviors and crowd congestion, via the identification and classification of these activities.

搜集汇总

背景与挑战

背景概述

Hajj and Umrah Event Recognition Datasets (HUER) 是一个由乌姆·阿尔-库拉大学创建的数据集，专注于朝觐和小朝仪式中的人类活动识别，包含2011-2012年的视频和图像数据，涵盖六种仪式事件和九种行为，旨在通过技术手段解决朝觐期间的安全和管理挑战。