{"title":"Estimation of Hand Posture with Straight Line Detection for a Hand Pose Rally System","authors":"Ayumu Meiji, A. Suganuma","doi":"10.12792/icisip2021.034","DOIUrl":null,"url":null,"abstract":"There is an event called \"stamp rally\" that collects checkpoint stamps. The event needs some rally tools. If a participant loses his/her rally tools, it will be difficult for him/her to continue the rally. We are developing the hand pose rally system, which is one of the \"gesture interface\". Our system attempts to identify an individual by the posture of the participant's hand captured by the USB camera. By bending and stretching five fingers, 32 types of hand postures are achieved. This system estimates which of the 32 types of hand postures whom he/she has presented. We have been constructing a posture estimation method. However, the accuracy was not so good depending on the posture of the hand. In this paper, we focus only on the posture estimation part of the hand pose rally system, and consider a method to improve the estimation accuracy.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12792/icisip2021.034","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
There is an event called "stamp rally" that collects checkpoint stamps. The event needs some rally tools. If a participant loses his/her rally tools, it will be difficult for him/her to continue the rally. We are developing the hand pose rally system, which is one of the "gesture interface". Our system attempts to identify an individual by the posture of the participant's hand captured by the USB camera. By bending and stretching five fingers, 32 types of hand postures are achieved. This system estimates which of the 32 types of hand postures whom he/she has presented. We have been constructing a posture estimation method. However, the accuracy was not so good depending on the posture of the hand. In this paper, we focus only on the posture estimation part of the hand pose rally system, and consider a method to improve the estimation accuracy.