用于人体运动分类的时间加权运动历史图像

IF 1.5 Q3 SPORT SCIENCES Sports Engineering Pub Date : 2023-10-24 DOI:10.1007/s12283-023-00437-1
Hideto Komori, Mariko Isogawa, Dan Mikami, Takasuke Nagai, Yoshimitsu Aoki
{"title":"用于人体运动分类的时间加权运动历史图像","authors":"Hideto Komori, Mariko Isogawa, Dan Mikami, Takasuke Nagai, Yoshimitsu Aoki","doi":"10.1007/s12283-023-00437-1","DOIUrl":null,"url":null,"abstract":"Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":1.5000,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-weighted motion history image for human activity classification in sports\",\"authors\":\"Hideto Komori, Mariko Isogawa, Dan Mikami, Takasuke Nagai, Yoshimitsu Aoki\",\"doi\":\"10.1007/s12283-023-00437-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.\",\"PeriodicalId\":46387,\"journal\":{\"name\":\"Sports Engineering\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sports Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s12283-023-00437-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SPORT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sports Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12283-023-00437-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

基于视觉的人类活动分类在运动环境中具有显著的应用潜力(例如,提高成绩的运动分析,运动员的主动感知等)。近年来,基于学习的人类活动分类得到了广泛的研究。然而,在需要更详细和针对特定球员的分类的体育场景中,这是一项相当具有挑战性的任务;在许多情况下,只有有限数量的数据集可用,不像走路或爬楼梯等日常运动。因此,本文提出了一种时间加权的运动历史图像,这是一种有效的图像序列表示,用于基于学习的人类活动分类。与传统的基于“新框架更重要”假设的MHI不同,我们的方法生成重要性感知表示,以便预测器可以“看到”有助于分析特定人类活动的框架。实验结果表明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Time-weighted motion history image for human activity classification in sports
Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Sports Engineering
Sports Engineering SPORT SCIENCES-
CiteScore
2.40
自引率
17.60%
发文量
23
期刊介绍: Sports Engineering is an international journal publishing original papers on the application of engineering and science to sport. The journal intends to fill the niche area which lies between classical engineering and sports science and aims to bridge the gap between the analysis of the equipment and of the athlete. Areas of interest include the mechanics and dynamics of sport, the analysis of movement, instrumentation, equipment design, surface interaction, materials and modelling. These topics may be applied to technology in almost any sport. The journal will be of particular interest to Engineering, Physics, Mathematics and Sports Science Departments and will act as a forum where research, industry and the sports sector can exchange knowledge and innovative ideas.
期刊最新文献
Estimating vertical ground reaction forces from plantar pressure using interpretable high-dimensional approximation Comparing equestrian helmets with and without rotational technology using an equestrian concussive-specific helmet test protocol Global navigation satellite systems’ receivers in mountain running: the elevation problem Power loss of the chain drive in a race tandem bicycle Concurrent validity and reliability of photoelectric and accelerometer technology for calculating vertical jump height in female athletes
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1