John Renz B. Bodollo, John Daniel V. Cortez, Edrick Raven P. Maraya, Ervin V. Navarro, Ralf Quintin L. Saquing, R. Tolentino
{"title":"Selection of Appliance Using Skeletal Tracking and 3D Face Tracking for Gesture Control Home Automation","authors":"John Renz B. Bodollo, John Daniel V. Cortez, Edrick Raven P. Maraya, Ervin V. Navarro, Ralf Quintin L. Saquing, R. Tolentino","doi":"10.1109/ICATIECE45860.2019.9063625","DOIUrl":null,"url":null,"abstract":"This study implements 3d face tracking and skeletal tracking in Microsoft Kinect Xbox One. Once the user is detected by the sensor head point from skeletal tracking, and a computed point D from chin point and eyebrow midpoint from 3d face tracking will be used to create the Line of Sight vector. Also, appliance points are always specified by the location of the appliance in the room with respect to the Kinect. Different appliance vectors will be created through vector subtraction. Angles between the Line of Sight vector and each of the appliance vector were computed through scalar product and compared to obtain the smallest angle. Once the smallest angle was obtained it was compared to a 15-degree threshold. If it’s within the threshold, then the appliance is selected.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICATIECE45860.2019.9063625","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
This study implements 3d face tracking and skeletal tracking in Microsoft Kinect Xbox One. Once the user is detected by the sensor head point from skeletal tracking, and a computed point D from chin point and eyebrow midpoint from 3d face tracking will be used to create the Line of Sight vector. Also, appliance points are always specified by the location of the appliance in the room with respect to the Kinect. Different appliance vectors will be created through vector subtraction. Angles between the Line of Sight vector and each of the appliance vector were computed through scalar product and compared to obtain the smallest angle. Once the smallest angle was obtained it was compared to a 15-degree threshold. If it’s within the threshold, then the appliance is selected.
本研究在微软Kinect Xbox One上实现了三维人脸跟踪和骨骼跟踪。一旦用户被传感器检测到头部点来自骨骼跟踪,从下巴点和眉毛中点从3d面部跟踪计算点D将被用来创建视线向量。此外,设备点总是由设备在房间中相对于Kinect的位置来指定。通过向量减法,将创建不同的器具向量。通过标量积计算视线矢量与各应用矢量之间的夹角,并进行比较,求出最小夹角。一旦获得最小的角度,就将其与15度阈值进行比较。如果在阈值范围内,则选择该设备。