基于改进深度信息的手势分割与定位
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摘要
针对基于Kinect深度信息分割的手势往往包含手腕易造成后续手势误识别的问题,提出一种改进深度信息的手势分割与定位算法。首先,基于深度信息阈值限定在实验空间中检测出手势二值图;然后,根据普通手势特征,提出基于手势端点检测和可变阈值算法分割出准确手势。为得到稳定的分割效果,对分割手势进行形态学处理,最后选取基于手势重心坐标和最大内切圆圆心坐标的联合手势定位法定位手势。实验结果表明,该手势分割方法比已有分割方法更准确可靠,联合手势定位比Kinect软件开发工具包骨骼数据定位和手势重心定位稳定,无奇异点。
Aiming at the problem that segmented gesture by Kinect depth information usually contains wrist data,which easily causes subsequent false gesture recognition,a gesture segmentation and positioning algorithm based on improved depth information was proposed. Firstly,the gesture binary image was detected based on depth information threshold limit in experimental space. Secondly,according to characteristics of common gestures,accurate gesture was segmented by gesture endpoint detection and variable threshold algorithm. In order to obtain stable segmentation results,morphological processing of segmented gesture was conducted. Lastly,the gesture positioning algorithm was proposed based on the method of combining gesture gravity center coordinates and maximum inscribed circle center coordinates. The experimental results show that the proposed gesture segmentation method has better accuracy and stability than the existing algorithm. The combined gesture positioning is more stable than gesture gravity center positioning and skeletal data positioning of Kinect Software Development Kit( SDK) and it has no singular points.
Aiming at the problem that segmented gesture by Kinect depth information usually contains wrist data,which easily causes subsequent false gesture recognition,a gesture segmentation and positioning algorithm based on improved depth information was proposed. Firstly,the gesture binary image was detected based on depth information threshold limit in experimental space. Secondly,according to characteristics of common gestures,accurate gesture was segmented by gesture endpoint detection and variable threshold algorithm. In order to obtain stable segmentation results,morphological processing of segmented gesture was conducted. Lastly,the gesture positioning algorithm was proposed based on the method of combining gesture gravity center coordinates and maximum inscribed circle center coordinates. The experimental results show that the proposed gesture segmentation method has better accuracy and stability than the existing algorithm. The combined gesture positioning is more stable than gesture gravity center positioning and skeletal data positioning of Kinect Software Development Kit( SDK) and it has no singular points.
引文
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[2]罗元,何超,王艳,等.基于深度预分割结合Camshift跟踪算法的动态手势识别方法[J].半导体光电,2015,36(1):155-159.(LUO Y,HE C,WANG Y,et al.A novel dynamic hand gesture recognition method based on depth pre-segmentation combining with Camshift tracking algorithm[J].Semiconductor Optoelectronics,2015,36(1):155-159.)
[3]麦健桦.基于自然手势的机器人控制[D].广州:华南理工大学,2013:12-14.(MAI J H.Robotic control based on natural gesture[D].Guangzhou:South China University of Technology,2013:12-14.)
[4]李振.基于手势识别的远程控制机械手的设计[D].武汉:武汉理工大学,2014:22-29.(LI Z.Design of remote control of manipulator based on gesture recognition[D].Wuhan:Wuhan University of Technology,2014:22-29.)
[5]YANG W J,WANG C M.Depth information and motion context feature based dynamic hand gesture detection and recognition[J].Journal of Information and Computational Science,2015,12(10):3775-3782.
[6]LEE T,HOLLERER T.Handy AR:markerless inspection of augmented reality objects using fingertip tracking[C]//Proceedings of the 2007 IEEE International Symposium on Wearable Computers.Washington,DC:IEEE Computer Society,2007:83-90.
[7]REN Z,YUAN J,MENG J,et al.Robust part-based hand gesture recognition using Kinect sensor[J].IEEE Transactions on Multimedia,2013,15(5):1110-1120.
[8]ELSAADANY O S,ABDELWAHAB M M.Real-time 2DHoG-2DPCA algorithm for hand gesture recognition[C]//ICIAP 2013:Proceedings of the 17th International Conference on Image Analysis and Processing,LNCS 8157.Berlin:Springer,2013:601-610.
[9]YAO Y,ZHANG F,FU Y.Real-time hand gesture recognition using RGB-D sensor[M]//Computer Vision and Machine Learning with RGB-D Sensors.New York:Springer International Publishing,2014:289-313.
[10]QIN S,ZHU X,YANG Y,et al.Real-time hand gesture recognition from depth images using convex shape decomposition method[J].Journal of Signal Processing Systems,2014,74(1):47-58.
[11]QIN S,ZHU X,YU H,et al.Real-time markerless hand gesture recognition with depth camera[M]//Advances in Multimedia Information Processing-PCM 2012.Berlin:Springer,2012:186-197.
[12]陈子毫.基于深度信息的手势检测与跟踪[D].广州:华南理工大学,2012:20-29.(CHEN Z H.Gesture detection and tracking based on depth information[D].Guangzhou:South China U-niversity of Technology,2012:20-29.)
[13]SUAREZ J,MURPHY R R.Hand gesture recognition with depth images:A review[C]//2012 IEEE RO-MAN:Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication.Piscataway,NJ:IEEE,2012:411-417.
[14]NIKHIL S,MOHAN S,RAMYA B,et al.Design and development of a DSP processor based reconfigurable hand gesture recognition system for real time applications[C]//Proceedings of the2010 International Conference on Signal and Image Processing.Piscataway,NJ:IEEE,2010:39-44.
[15]张世辉,张煜婕,孔令富.一种基于深度图像的自遮挡检测方法[J].小型微型计算机系统,2010,31(5):964-968.(ZHANG S H,ZHANG Y J,KONG L F.Self-occlusion detection approach based on depth image[J].Journal of Chinese Computer Systems,2010,31(5):964-968.)