Posture recognition method based on Kinect predefined bone

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3441-3445.

• Artificial intelligence • Previous Articles Next Articles

Posture recognition method based on Kinect predefined bone

ZHANG Dan¹,CHEN Xingwen¹,ZHAO Shuying²,LI Jiwei²,BAI Yu³

1. Innovation Education Center, Dalian Nationalities University, Dalian Liaoning 116600,China;
2. College of Information Science and Engineering, Northeastern University, Shenyang Liaoning 110819, China;
3. College of Information and Communication Engineering, Dalian Nationalities University, Dalian Liaoning 116600, China

Received:2014-06-27 Revised:2014-09-10 Online:2014-12-31 Published:2014-12-01
Contact: ZHANG Dan

基于Kinect骨骼预定义的体态识别算法

张丹¹,陈兴文¹,赵姝颖²,李纪伟²,白钰³

1. 大连民族学院创新教育中心,辽宁大连 116600
2. 东北大学信息科学与工程学院,沈阳 110819;
3. 大连民族学院信息与通信工程学院,辽宁大连 116600

通讯作者: 张丹
作者简介:张丹(1987-),女,辽宁大连人,工程师,硕士,主要研究方向:图像处理、机器视觉;陈兴文(1969-),男,辽宁大连人,教授,主要研究方向:图像处理、模式识别；赵姝颖(1968-),女,辽宁沈阳人,教授,博士,主要研究方向:图像处理、计算机视觉；李纪伟(1987-）,男,河南郑州人,工程师,主要研究方向:计算机视觉、人机交互。
基金资助:
中央高校基础科研基金资助项目;机器人学国家重点实验室开放基金资助项目

Abstract

Abstract:

In view of the problems that posture recognition based on vision requires a lot on environment and has low anti-interference capacity, a posture recognition method based on predefined bone was proposed. The algorithm detected human body by combining Kinect multi-scale depth and gradient information. And it recognized every part of body based on random forest which used positive and negative samples, built the body posture vector. According to the posture category, optimal separating hyperplane and kernel function were built by using improved support vector machine to classify postures. The experimental results show that the recognition rate of this scheme is 94.3%, and it has good real-time performance, strong anti-interference, good robustness, etc.

摘要：

针对基于视觉的体态识别对环境要求较高、抗干扰性差等问题,提出了一种基于人体骨骼预定义的识别分类方法。该算法结合Kinect多尺度深度信息和梯度信息检测人体；基于随机森林采用正负样本互限思想识别人体各个部分,根据各部分距离构建人体姿态向量,识别骨架；再根据体态类别,构建最优分类超平面、核函数,采用改进的支持向量机进行体态分类。实验结果表明,所提算法的分类识别准确率可达94.3%,具有实时性好,抗干扰性强,鲁棒性较好等特点。

CLC Number:

TP391

ZHANG Dan CHEN Xingwen ZHAO Shuying LI Jiwei BAI Yu. Posture recognition method based on Kinect predefined bone[J]. Journal of Computer Applications, 2014, 34(12): 3441-3445.

张丹陈兴文赵姝颖李纪伟白钰. 基于Kinect骨骼预定义的体态识别算法[J]. 计算机应用, 2014, 34(12): 3441-3445.

References

［1］RAO C, YILMAZ A, SHAH M. View-invariant representation and recognition of actions［J］. International Journal of Computer Vision, 2002,50(2): 203-226.
［2］PARAMESWARAN V, CHELLAPPA R. View invariants for human action recognition［C］// Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison. Washington, DC: IEEE Computer Society, 2003: 1-7.
［3］AGGARWAL J K, CAI Q. Human motion analysis: a review［J］. Computer Vision and Image Understanding, 2005, 73(3): 428-440.
［4］THOMAS B M, ERIK G. A survey of computer vision-based human motion capture［J］. Computer Vision and Image Understanding, 2001,81(3): 231-268.
［5］WANG L, HU W M, TIE N T. Recent developments in human motion analysis［J］. Pattern Recognition, 2003, 36(3):585-601.
［6］WANG L, TAN T, NING H, et al. Silhouette analysis-based gait recognition for human identification［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12):1505-1518.
［7］CAMPBELL L W, BECKER D A, AZARBAYEJANI A, et al. In-variant features for 3D gesture recognition［C］// Proceedings of the 1996 International Conference on Automatic Face and Gesture Recognition. Piscataway: IEEE, 1996:157-162.
［8］KALAL Z, MATAS J, MIKOLAJCZYK K. Online learning of robust object detectors during unstable tracking［C］// Proceedings of the 3rd On-line Learning for Computer Vision Workshop. Washington, DC: IEEE Computer Society, 2009:165-173.
［9］OJALA T, PIETIKAINEN M, MAENPAA T. Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns［J］. International Journal of Computer Vision and Pattern Recognition, 2002, 24(7):971-987.
［10］RAHEJA J L, CHAUDHARV A, SIGNAL K. Tracking of fingertips and centers of palm using Kinect［C］// Proceedings of the 3rd International Conference on Computational Intelligence, Modeling and Simulation. Piscataway: IEEE, 2011:248-252.
［11］Open Kinect Organization. Imaging information for Kinect［EB/OL］.［2011-07-10］. http://openkinect.org/wiki/ImagingInformation.
［12］GU R, HE Y, JIN L. Design and implementation of Kinect skeletal tracking technology-based intelligent teaching control system［J］. Computer Application and Software,2013,30(6):276-280.(顾容,何益明,金丽燕. 基于Kinect骨骼追踪技术的智能教学控制系统的设计与实现［J］.计算机应用与软件, 2013,30(6):276-280.)

[1]	. Pavement defect detection algorithm with enhanced morphological perception [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	YANG Jianfeng, CHEN Bin, LI Yuxuan. Self-supervised point cloud anomaly detection method based on point cloud reconstruction [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	XUE Zhenhua, LI Qiang, HUANG Chao. Vision foundation model-driven pixel-level image anomaly detection model [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Binocular vision object location algorithm for robot arm grasping [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Multi-attention contrastive learning for infrared small target detection [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	LI Zhonghua, ZHONG Gengxin, FAN Ping, ZHU Hengliang. Camouflaged object detection by boundary mining and background guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[7]	. Multi-view difficult airway recognition based on discriminant region guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[8]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Gravity data denoising method based on multilevel wavelet residual network#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Data augmentation method for abnormal passenger behavior in elevators based on dynamic graph convolutional network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Text-based person retrieval method based on multi-granularity shared semantic center association [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	ZHANG Qing, YANG Fan, FANG Yuhan. Chinese Spelling Correction Algorithm Based on Multi-Modal Information Fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	WANG Hao, WANG Jinwei, CHENG Xin, ZHANG Jiawei, WU Hao, LUO Xiangyang, MA Bin. A Review of JPEG Recompression Forensics for Color Images [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Distribution adaptation and dynamic curriculum pseudo-labeling framework for semi-supervised fire detection [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Multimodal harmful content detection method based on weakly supervised modality semantic enhancement [J]. Journal of Computer Applications, 0, (): 0-0.

Posture recognition method based on Kinect predefined bone

基于Kinect骨骼预定义的体态识别算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics