面向视频拥挤场景的朝觐和副朝地点分类系统
A Hajj And Umrah Location Classification System For Video Crowded Scenes
摘要：
在本文中，一个新的自动系统，在不同的朝觐和副朝视频场景的仪式地点进行分类。这个具有挑战性的主题在过去由于几个问题而被忽视，其中之一是缺乏真实的注释视频数据集。HUER数据集被定义为模拟六个不同的朝觐和副朝仪式地点[26]。建议朝觐和副朝仪式地点分类系统包括四个主要阶段：预处理，分割，特征提取，和位置分类阶段。镜头边界检测和背景/前景分割算法被应用于准备输入视频场景到KNN，ANN和SVM分类器。该系统改进了朝觐和副朝位置分类的最新结果，并成功识别了六个朝觐仪式，准确率超过90%。各种演示实验显示了有希望的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
