文献类型：会议论文

英文题名：Decision-tree-based Algorithm for 3D Sign Classification

作者：Yao, Dengfeng[1,3];Jiang, Minghu[1];Abulizi, Abudoukelimu[1];You, Xu[2]

第一作者：姚登峰;Yao, Dengfeng

通讯作者：Yao, DF[1]

机构：[1]Tsinghua Univ, Sch Humanities, Lab Computat Linguist, Beijing 10084, Peoples R China;[2]Beijing Univ Aeronaut & Astronaut, Dept Math, Beijing 100191, Peoples R China;[3]Beijing Union Univ, Special Educ Sch, Beijing 100075, Peoples R China

第一机构：Tsinghua Univ, Sch Humanities, Lab Computat Linguist, Beijing 10084, Peoples R China

通讯机构：[1]corresponding author), Tsinghua Univ, Sch Humanities, Lab Computat Linguist, Beijing 10084, Peoples R China.

会议论文集：12th IEEE International Conference on Signal Processing (ICSP)

会议日期：OCT 19-23, 2014

会议地点：HangZhou, PEOPLES R CHINA

语种：英文

外文关键词：decision tree; 3D-based recognition; Chinese sign language

摘要：Sign recognition has evolved from traditional video-based to 3D-based image recognition. Most documents are presented with Kinect-based somatosensory terminals, which are limited by difficulties in precisely describing the motions performed by various palm joints. The linguistic details of sign language (SL), such as position, direction, and movement, therefore have to be manually inputted. Meanwhile, most studies rely on the positions or rotation of virtual agent articulations as experimental data to apply classifying or matching techniques, which employ inefficient algorithms. By fully utilizing the features of Leap Motion, motion trajectory is automatically calculated on a computer. Such features as location, movement, and direction are calculated on the basis of the motion parameters of 22 palm joints. Thus, we propose a decision-tree-based algorithm to recognize 3D gestures. Our experimental results show that 1,203 Chinese SLs were signed, and 1,152 were successfully recognized with the use of the Leap Motion sensor. Thus, the recognition rate reached 95.8%, with a recognition response time of only 5.4 s.