一种自适应关节点权值的姿势相似度计算方法
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摘 要: 提出了一种新的关节点权值自适应的姿势相似度计算方法,选用Kinect体感设备采集姿势信息,获取人体骨架关节点数据.为适应不同人体体型,根据骨架长度对关节点数据进行修正.另外,针对不同的人体姿势,提出自适应的关节点权值定义方法.实验结果表明:所提出的姿势相似度计算方法准确度高并且结果稳定.
关键词: 关节点权值; 源数据修正; Kinect; 权值自适应; 姿势相似度
中图分类号: TP 391.4 文献标志码: A 文章编号: 10005137(2019)04035606
Abstract: A novel posture similarity calculation method using selfadaptive joint weight was proposed in this paper.Kinect was selected to collect posture information,using which the human skeleton joint data was acquired.In order to accommodate various body shapes,the data of joints was modified according to the length of skeletons.In addition,the definition of selfadaptive joint weight was proposed in terms of various human postures.The experimental results showed that the proposed posture similarity calculation method achieved high accuracy and stable results.
Key words: joint weight; source data modification; Kinect; weight selfadaptation; posture similarity
0 引 言
3 結 论
本文提出了一种新的自适应关节点权值的姿势相似度计算方法,该方法以模板姿势关节点为基础,对待测试姿势的关节点进行调整,有效解决了不同体型、位置之间的姿势预处理问题.以模板姿势的骨架长度为参考,给每个关节点增加一个权值,计算姿势相似度.实验结果表明:所提出的姿势相似度计算方法效果较好.
参考文献:
[1] SAHOO S P,ARI S.On an algorithm for human action recognition [J].Expert Systems with Applications,2019,115:524-534.
[2] MAITY S,BHATTACHARJEE D,CHAKRABARTI A.A novel approach for human action recognition from silhouette images [J].IETE Journal of Research,2017,63(2):160-171.
[3] BEAUDRY C,PTERI R,MASCARILLA L.An efficient and sparse approach for large scale human action recognition in videos [J].Machine Vision and Applications,2016,27(4):529-543.
[4] MOUSSA M M,HAMAYED E,FAYEK M B,et al.An enhanced method for human action recognition [J].Journal of Advanced Research,2015,6(2):163-169.
[5] ASTERIADIS S,DARAS P.Landmarkbased multimodal human action recognition [J].Mutimedia Toolsans Applications,2017,76(3):4505-4521.
[6] XU Z Y,ZHANG Q N,CHENG S Y.Multilevel active registration for kinect human body scans:from low quality to high quality [J].Multimedia Systems,2018,24(3):257-270.
[7] SEDDIK B,GAZZAH S,AMARA N E B.Humanaction recognition using a multilayered fusionscheme of Kinect modalities [J].IET Computer Vision,2017,11(7):530-540.
[8] LIU X P,LI Y B,LI X M,et al.Multifeature consultation model for human action recognition in depth video sequence [J].The Journal of Engineering,2018,(16):1498-1502.
[9] GAVRILOVA M L,WANG Y X,AHMED F,et al.Kinect sensor gesture and activity recognition:new applications for consumer cognitive systems [J].IEEE Consumer Electronics Magazine,2017,7(1):88-94.
[10] LI Q N,WANG Y F,SHARF A,et al.Classification of gait anomalies from Kinect [J].Visual Computer,2018,34(2):229-241.
[11] THANH T T,CHEN F,KOTANI K,et al.Extraction of discriminative patterns from skeleton sequences for human action recognition [C]//IEEE RIVF International Conference on Computing and Communication Technologies Research Innovation and Vision for the Future.Ho Chi Minh:IEEE,2014:1-6.
[12] GIRSHICK R,SHOTTON J,KOHLI P,et al.Efficient regression of generalactivity human poses from depth images [C]//International Conference on Computer Vision.Barcelona:IEEE,2011:415-422.
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