References
- Vicon Motion Capture System. https://www.vicon.com
- OptiTrack Motion Capture System. http://optitrak.com
-
E. de Aguiar et al., "
$M^3$ : Marker-Free Model Reconstruction and Motion Tracking from 3D Voxel Data," Conf. Comput. Graph. Applicat., Los Alamitos, CA, USA, Oct. 6-8, 2004, pp. 101-110. - B. Michoud et al., "Real-Time Marker-Free Motion Capture from Multiple Cameras," Int. Conf. Comput. Vis., Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp. 1-7.
- S. Corzaaz et al., "A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach," Ann. Biomed. Eng., vol. 34, no. 6, June 2006, pp. 1019-1029. https://doi.org/10.1007/s10439-006-9122-8
- E.D. Aguiar et al., "Marker-Less Deformable Mesh Tracking for Human Shape and Motion Capture," IEEE Conf. Comput. Vis. Pattern Recogn., Minneapolis, MN, USA, June 17-22, 2007, pp. 1-8.
- J. Gall et al., "Motion Capture Using Joint Skeleton Tracking and Surface Estimation," IEEE Conf. Comput. Vis. Pattern Recogn., Miami, FL, USA, June 20-25, 2009, pp. 1746-1753.
- Microsoft Kinect Camera. https://developer.microsoft.com/enus/windows/kinect
- L. Chen, H. Wei, and J. Ferryman, "A Survey of Human Motion Analysis Using Depth Imagery," Pattern Recogn. Lett., vol. 23, no. 15, Nov. 2013, pp. 1995-2006.
- D.S. Alexiadis et al., "Evaluating a Dancer's Performance Using Kinect-Based Skeleton Tracking," Proc. ACM. Int. Conf. Multimedia, Scottsdale, AZ, USA, Nov. 28-Dec. 1, 2011, pp. 659-662.
- S. Izadi et al., "KinectFusion: Real-Time 3D Reconstruction and Interaction Using a Moving Depth Camera," Proc. Annu. ACM Symp. User Interface Softw. Technol., Santa Barbara, CA, USA, Oct. 16-19, 2011, pp. 559-568.
- E. Auvinet, J. Meunier, and F. Multon, "Multiple Depth Cameras Calibration and Body Volume Reconstruction for Gait Analysis," Int Conf. Inform. Sci., Signal Process. Their Applicat., Montreal, Canada, July 2-5, 2012, pp. 478-483.
- K. Berger et al., Markerless Motion Capture Using Multiple Color-Depth Sensors, Vision, Modeling, and Visualization, Airela Ville, Switzerland: The Eurographics Association, 2011, pp. 317-324.
- L. Zhang et al., "Real-Time Human Motion Tracking Using Multiple Depth Cameras," IEEE Int. Conf. Intell. Robots Syst., Vilamoura-Algarve, Portugal, Oct. 7-12, 2012, pp. 2389-2395.
- B. Williamson et al., Multi-kinect Tracking for Dismounted Soldier Training, Interservice/Ind. Training, Simulation, Educ. Conf., Orlando, FL, USA, Dec. 3-6, 2012, pp. 1-9.
- S. Asteriadis et al., "Estimating Human Motion from Multiple Kinect Sensors," Proc. Int. Conf. Comput. Vis. Comput. Graph.s Collaboration Techn. Applicat., Berlin, Germany, June 6-7, 2013, pp. 3-8.
- A. Kitsikidis et al., "Dance Analysis Using Multiple Kinect Sensors," Int. Conf. Comput. Vis. Theory Applicat., Lisbon, Portugal, Jan. 5-8, 2014, pp. 789-795.
- S. Kaenchan et al., "Automatic Multiple Kinect Cameras Setting for Simple Walking Posture Analysis," Int. Comput. Sci. Eng. Conf., Bangkok, Thailand, Sept. 4-6, 2013, pp. 245-249.
- S. Moon et al., "Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering," Int. J. Adv. Robot. Syst., vol. 13, 2016, pp. 1-10. https://doi.org/10.5772/62058
- H. Jo et al., "Motion Tracking System for Multi-user with Multiple Kinects," Int. J. u- e-Service, Sci. Technol., vol. 8, no. 7, 2015, pp. 99-108. https://doi.org/10.14257/ijunesst.2015.8.7.10
- N. Ahmed, Unified Skeletal Animation Reconstruction with Multiple Kinects, Aire-la Ville, Switzerland: the Eurographics Association, 2014, pp. 5-8.
- S. Baek and M. Kim, "Dance Experience System Using Multiple Kinects," Int. J. Future Comput. Commun., vol. 4, no. 1, Feb. 2015, pp. 45-49. https://doi.org/10.7763/IJFCC.2015.V4.353
- P.J. Besl and N.D. McKay, "A Method for Registration of 3-D Shapes," Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, Feb. 1992, pp. 239-256. https://doi.org/10.1109/34.121791
- W.H. Press et al., Numerical Recipes in C++: the Art of Scientific Computing, New York, USA: Cambridge University Press, 2002.
- Xsens MVN Motion Capture System. http://xsens.com
- Libgreenect2. https://openkinect.github.io/libfreenect2/
- J. Shotton et al., "Real-Time Human Pose Estimation in Parts from Single Depth Images," Commun. ACM, vol. 56, no. 1, Jan. 2013, pp. 116-124. https://doi.org/10.1145/2398356.2398381
Cited by
- Development of Dance Learning System Using Human Depth Information vol.18, pp.8, 2017, https://doi.org/10.9728/dcs.2017.18.8.1627
- On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor vol.18, pp.3, 2017, https://doi.org/10.3390/s18030913
- Empirical study of the usability and interactivity of an augmented-reality dressing mirror vol.24, pp.10, 2018, https://doi.org/10.1007/s00542-018-3879-1
- Dynamic Pose Estimation Using Multiple RGB-D Cameras vol.18, pp.11, 2017, https://doi.org/10.3390/s18113865
- Predicting Human Actions Taking into Account Object Affordances vol.93, pp.3, 2017, https://doi.org/10.1007/s10846-018-0815-7
- Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning vol.19, pp.7, 2017, https://doi.org/10.3390/s19071716
- Monitoring Human Induced Floor Vibrations for Quantifying Dance Moves: A Study of Human-Structure Interaction vol.6, pp.None, 2017, https://doi.org/10.3389/fbuil.2020.00036
- Flexible Piezoelectric Nanofibers/Polydimethylsiloxane‐Based Pressure Sensor for Self‐Powered Human Motion Monitoring vol.8, pp.3, 2017, https://doi.org/10.1002/ente.201901242
- Multi‐dimensional data modelling of video image action recognition and motion capture in deep learning framework vol.14, pp.7, 2020, https://doi.org/10.1049/iet-ipr.2019.0588
- Robust Feature Matching with Spatial Smoothness Constraints vol.12, pp.19, 2020, https://doi.org/10.3390/rs12193158
- Optimization of Interactive Animation Capture System for Human Upper Limb Movement Based on XSENS Sensor vol.2021, pp.None, 2021, https://doi.org/10.1155/2021/5246438
- Vibrotactile-Based Operational Guidance System for Space Science Experiments vol.10, pp.9, 2017, https://doi.org/10.3390/act10090229
- Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods vol.11, pp.19, 2021, https://doi.org/10.3390/app11199096
- VR-Based Job Training System Using Tangible Interactions vol.21, pp.20, 2017, https://doi.org/10.3390/s21206794