Frame based approach for automatic event boundary detection of soccer video using optical flow

Devang S. Pandya, M. Zaveri
{"title":"Frame based approach for automatic event boundary detection of soccer video using optical flow","authors":"Devang S. Pandya, M. Zaveri","doi":"10.1109/ICSIPA.2017.8120644","DOIUrl":null,"url":null,"abstract":"Due to rapid growth of digital contents, there has been an increasing demand of summarized videos to save time and other network resources. Automated soccer video analysis is a challenging due to involvement of more actors and rapid movements of players and camera. It is necessary first to detect various events of the video precisely before analyzing and labeling them. This paper proposes frame based approach for the automatic demarcation of events of the soccer video. However this task is very challenging due to variety of soccer leagues, various illumination and ground conditions. To overcome such issues we propose method which is invariant to such conditions and can successfully demarcate the soccer events. We exploit optical flow techniques to measure the motion. We introduce change in optical flow to extract the behavior of an event over the video span. Later, adaptive threshold is computed based on change in optical flow. We conducted number of simulations with variety of videos to validate the method. Proposed method achieves nearly 90% of accuracy and found robust in spite of illumination variation.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIPA.2017.8120644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Due to rapid growth of digital contents, there has been an increasing demand of summarized videos to save time and other network resources. Automated soccer video analysis is a challenging due to involvement of more actors and rapid movements of players and camera. It is necessary first to detect various events of the video precisely before analyzing and labeling them. This paper proposes frame based approach for the automatic demarcation of events of the soccer video. However this task is very challenging due to variety of soccer leagues, various illumination and ground conditions. To overcome such issues we propose method which is invariant to such conditions and can successfully demarcate the soccer events. We exploit optical flow techniques to measure the motion. We introduce change in optical flow to extract the behavior of an event over the video span. Later, adaptive threshold is computed based on change in optical flow. We conducted number of simulations with variety of videos to validate the method. Proposed method achieves nearly 90% of accuracy and found robust in spite of illumination variation.
基于帧的足球视频光流事件边界自动检测方法
由于数字内容的快速增长,人们对视频摘要的需求越来越大,以节省时间和其他网络资源。自动化足球视频分析是一个具有挑战性的,因为涉及更多的演员和快速移动的球员和摄像机。首先要对视频中的各种事件进行准确的检测,然后再对其进行分析和标记。提出了一种基于帧的足球视频事件自动标定方法。然而,由于各种足球联赛,各种照明和地面条件,这项任务非常具有挑战性。为了克服这些问题,我们提出了一种不受这些条件影响的方法,可以成功地划分足球项目。我们利用光流技术来测量运动。我们引入光流的变化来提取事件在视频跨度上的行为。然后根据光流的变化计算自适应阈值。我们用各种视频进行了大量的模拟来验证该方法。该方法的准确率接近90%,且在光照变化情况下具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信