{"title":"A Survey on Automated Human Action Recognition Using Multi view Feature","authors":"S. Ashwini, Varalatchoumy","doi":"10.23883/ijrter.2019.5087.evu6a","DOIUrl":null,"url":null,"abstract":"— Recognizing the human action plays a significant role in surveillance cameras. Usually cameras are situated at distant place and convey actions in form of signals at one particular place. This paper presents a framework for recognizing a sequence of actions based on multi-view video data. To depict various actions activities performed in various perspectives, view-invariant feature is being used. The features of multi-view are extracted from various temporal scales, which are demonstrated using global spatial-temporal distribution. The proposed system performs is designed to work on cross tested datasets wherein the system doesn’t require retraining for same scenario that occurs multiple times.","PeriodicalId":143099,"journal":{"name":"INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23883/ijrter.2019.5087.evu6a","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
— Recognizing the human action plays a significant role in surveillance cameras. Usually cameras are situated at distant place and convey actions in form of signals at one particular place. This paper presents a framework for recognizing a sequence of actions based on multi-view video data. To depict various actions activities performed in various perspectives, view-invariant feature is being used. The features of multi-view are extracted from various temporal scales, which are demonstrated using global spatial-temporal distribution. The proposed system performs is designed to work on cross tested datasets wherein the system doesn’t require retraining for same scenario that occurs multiple times.