{"title":"UAV flight controlling based on Kinect for Windows v2","authors":"Mengjiao Dong, Lin Cao, Dong-Ming Zhang, Ru Guo","doi":"10.1109/CISP-BMEI.2016.7852806","DOIUrl":null,"url":null,"abstract":"Microsoft Kinect is a motion sensing device that provides users friendly interfaces through natural postures and gestures. In this paper, based on Kinect v2, a system is proposed to control UAV flight, without remote control. In this system, more postures and gestures are defined, thus to easily control UAV flight, than the existing works. Furthermore, the false recognition on some postures are analyzed, meanwhile MPRA (many parameters restriction algorithm) is presented to improve the precision by imposing multiple restriction on posture recognition. The evaluation platform is built based on Kinect v2 and Ar. Drone2.0, and then the experiments are carried out, which show that the proposed system outperforms the existing works.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP-BMEI.2016.7852806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Microsoft Kinect is a motion sensing device that provides users friendly interfaces through natural postures and gestures. In this paper, based on Kinect v2, a system is proposed to control UAV flight, without remote control. In this system, more postures and gestures are defined, thus to easily control UAV flight, than the existing works. Furthermore, the false recognition on some postures are analyzed, meanwhile MPRA (many parameters restriction algorithm) is presented to improve the precision by imposing multiple restriction on posture recognition. The evaluation platform is built based on Kinect v2 and Ar. Drone2.0, and then the experiments are carried out, which show that the proposed system outperforms the existing works.