3D Human Pose Tracking Approach Based on Double Kinect Sensors

doi:10.16182/j.issn1004731x.joss.19-0017

Journal of System Simulation ›› 2020, Vol. 32 ›› Issue (8): 1446-1454.doi: 10.16182/j.issn1004731x.joss.19-0017

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3D Human Pose Tracking Approach Based on Double Kinect Sensors

Li Qi^1,2, Wang Xiangdong³, Li Hua^1,2

1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. China Institute of Sport Science, General Administration of Sport, Beijing 100061, China

Received:2019-01-09 Revised:2019-03-12 Online:2020-08-18 Published:2020-08-13

Abstract

Abstract: 3D human pose estimation and tracking is a popular problem of machine vision. In order to improve the stability of human pose tracking, the human body motion posture is represented by the human joint DOF (Degree of Freedom) vector, the human pose is tracked by the unscented Kalman filter method, and a human pose tracking system based on double Kinect sensors is built. Compared with the traditional human motion capture system, the system can accurately and stably track 3D human pose under simple movements, reflect the special nature of the motion process under complex movements, and can be used in the performance evaluation of sports biomechanics.

Key words: 3D human pose tracking, Kinect sensor, human joint DOF, unscented Kalman filter, sports biomechanics

CLC Number:

TP391

Li Qi, Wang Xiangdong, Li Hua. 3D Human Pose Tracking Approach Based on Double Kinect Sensors[J]. Journal of System Simulation, 2020, 32(8): 1446-1454.

References 13

[1]	Moeslund T B, Hilton A, Krüger V.A Survey of Advances in Vision-based Human Motion Capture and Analysis[J]. Computer Vision and Image Understanding (S1077-3142), 2006, 104(2/3): 90-126.
[2]	Shotton J, Girshick R, Fitzgibbon A, et al.Efficient Human Pose Estimation from Single Depth Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2013, 35(12): 2821-2840.
[3]	Wei X, Zhang P, Chai J.Accurate Real-time Full-body Motion Capture Using a Single Depth Camera[J]. ACM Transactions on Graphics (S0730-0301), 2012, 31(6): 188.
[4]	Shuai L, Li C, Guo X, et al.Motion Capture with Ellipsoidal Skeleton Using Multiple Depth Cameras[J]. IEEE Transactions on Visualization and Computer Graphics (S1077-2626), 2017, 23(2): 1085-1098.
[5]	Guess T M, Razu S, Jahandar A, et al.Comparison of 3D Joint Angles Measured with the Kinect 2.0 Skeletal Tracker versus a Marker-based Motion Capture System[J]. Journal of Applied Biomechanics (S1065-8483), 2017, 33(2): 176-181.
[6]	Moon S, Park Y, Ko D W, et al.Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering[J]. International Journal of Advanced Robotic Systems (S1729-8814), 2016, 13(2): 65.
[7]	Joo H, Simon T, Li X, et al.Panoptic Studio: A Massively Multi-view System for Social Interaction Capture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2019, 41(1): 190-204.
[8]	Ionescu C, Papava D, Olaru V, et al.Human3. 6m: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2014, 36(7): 1325-1339.
[9]	Zhou X, Huang Q, Sun X, et al.Towards 3D Human Pose Estimation in the Wild: A Weakly-supervised Approach[C]//IEEE International Conference on Computer Vision. New York: IEEE Press, 2017: 398-407.
[10]	Bregler C, Malik J.Tracking People with Twists and Exponential Maps[C]//IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 1998: 8-15.
[11]	Taylor G W, Hinton G E, Roweis S T.Modeling Human Motion Using Binary Latent Variables[C]//Advances in Neural Information Processing Systems. Cambridge Massachusetts: MIT Press, 2007: 1345-1352.
[12]	Wan E A, van Der Merwe R. The Unscented Kalman Filter for Nonlinear Estimation[C]//Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. New York: IEEE Press, 2000: 153-158.
[13]	van Der Merwe R. Sigma-point Kalman Filters for Probabilistic Inference in Dynamic State-space Models [D]. 2004.

3D Human Pose Tracking Approach Based on Double Kinect Sensors

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