Model-based reinforcement of kinect depth data for human motion capture applications

Sensors (Switzerland)

Volumn 13, Issue 7, 2013, Pages 8835-8855

  Calderita, Luis Vicente ^a   Bandera, Juan Pedro ^b   Bustos, Pablo ^a   Skiadopoulos, Andreas ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Human motion capture; Pose analysis; Range camera; RGB D sensors; Sensor

Indexed keywords

KINEMATICS;

COMPUTATIONAL COSTS; HUMAN MOTION CAPTURE; HUMAN MOTIONS; MOTION CAPTURE SYSTEM; OPEN SOURCE FRAMEWORKS; POSE ANALYSIS; RANGE CAMERAS; RGB-D SENSORS;

SENSORS;

EID: 84880151819     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s130708835     Document Type: Article

References (27)

1. Moeslund, T.; Hilton, A.; Kr̈uger, V. A survey of advances in vision-based human motion capture and analysis. Comput. Vision Image Underst. 2006, 104, 90-126.
2. Bandera, J.P. Vision-Based Gesture Recognition in a Robot Learning by Imitation Framework. Ph.D. Thesis, Dpto. Tecnoloǵi{dotless}a Electŕonica, Universidad de Ḿalaga, Spain, Universidad de Ḿalaga, Spain, January 2010.
3. Calderita, L.; Manso, L.; Ńunez, P. Assessment of PrimeSense RGB-D Camera for Robotic Applications. In Proceedings of the XIII Workshop of Physical Agents 2012 (WAF 2012), Santiago de Compostela, Spain, 3-4 September 2012; pp. 147-153.
4. Agarwal, A.; Triggs, B. Recovering 3D human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 44-58.
5. Poppe, R. Vision-based human motion analysis: An overview. Comput. Vision Image Underst. 2007, 108, 4-18.
6. Mori, G.; Malik, J. Estimating human body configurations using shape context matching. Lect. Notes Comput. Sci. 2002, 2352, 666-674.
7. Demirdjian, D.; Ko, T.; Darrell, T. Untethered gesture acquisition and recognition for virtual world manipulation. Virtual Real. 2005, 8, 222-230.
8. Shotton, J.; Sharp, T.; Kipman, A.; Fitzgibbon, A.; Finocchio, M.; Blake, A.; Cook, M.; Moore, R. Real-time human pose recognition in parts from single depth images. ACM Commun. 2013, 56, 116-124.
9. Zhang, L.; Sturm, J.; Cremers, D.; Lee, D. Real-Time Human Motion Tracking Using Multiple Depth Cameras. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamoura, Portugal, 7-12 October 2012; pp. 2389-2395.
10. Stenger, B.; Mendonca, P.R.S.; Cipolla, R. Model-Based Hand Tracking Using an Unscented Kalman Filter. In Proceedings of the British Machine Vision Conference, Manchester, UK, 22-24 September 2001; pp. 63-72.
11. Sminchisescu, C.; Triggs, B. Estimating articulated human motion with covariance scaled sampling. Int. J. Robot. Res. 2003, 22, 371-391.
12. Azad, P.; Ude, A.; Asfour, T.; Dillmann, R. Stereo-Based Markerless Human Motion Capture for Humanoid Robot Systems. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2007), Roma, Italy, 10-14 April 2007; pp. 3951-3956.
13. Starck, J.; Hilton, A. Model-based human shape reconstruction from multiple views. Comput. Vision Image Underst. 2008, 111, 179-194.
14. Grauman, K.; Shakhnarovich, G.; Darrell, T. Inferring 3D Structure with a Statistical Image-Based Shape Model. In Proceedings of the International Conference on Computer Vision, Nice, France, 13-16 October 2003; pp. 641-647.
15. Mikíc, I.; Trivedi, M.; Hunter, E.; Cosman, P. Human body model acquisition and tracking using voxel data. Int. J. Comput. Vision 2003, 53, 199-223.
16. Ganaphathi, V.; Plagemann, C.; Koller, D.; Thrun, S. Real Time Motion Capture Using a Single Time-of-Flight Camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 13-18 June 2010; pp. 755-762.
17. Deutscher, J.; Reid, I. Articulated body motion capture by stochastic search. Int. J. Comput. Vision 2005, 61, 185-205.
18. Yamamoto, M.; Yagishita, K. Scene Constraintsaided Tracking of Human Body. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, 13-15 June 2000; pp. 151-156.
19. Contini, R. Body segment parameters, Part II. Artif. Limbs 1972, 16, 1-19.
20. Yoon, S.E.; Salomon, B.; Lin, M.; Manocha, D. Fast Collision Detection between Massive Models Using Dynamic Simplification. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, Nice, France, 8-10 July 2004; pp. 136-146.
21. Mitchelson, J. Multiple-Camera Studio Methods for Automated Measurement of Human Motion. Ph.D. Thesis, University of Surrey, Surrey, UK, September 2003.
22. Huang, Q.; Yokoi, K.; Kajita, S.; Kaneko, K.; Arai, H.; Koyachi, N.; Tanie, K. Planning walking patterns for a biped robot. IEEE Trans. Robot. Autom. 2001, 17, 280-289.
23. Tang, Z.; Zhou, C.; Sun, Z. Gait planning for soccer playing humanoid robots. Lect. Notes Control Inf. Sci. 2004, 299, 241-262.
24. Shreiner, D.; Woo, M.; Neider, J.; Davis, T. OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2, 5th ed.; Addison-Wesley Professional: Boston, MA, USA, 2005.
25. Innovision Systems Inc. MaxPro: First of Its Kind Motion Analysis Software for 3D Motion Capture Using Non Proprietary Cameras. Available online: http://www.innovisionsystems.com/Products/MaxPro.html (accessed on 9 July 2013).
26. Huang, V.; Krakauer, J. Robotic neurorehabilitation: A computational motor learning perspective. J. NeuroEng. Rehabil. 2009, 6, doi:10.1186/1743-0003-6-5.
27. Naranjo-Saucedo, A.; Súarez, C.; Bustos, P.; Calderita, L.; Cintas, R.; Bachiller, P.; Parra-Caldeŕon, C. Design of a Social Robot as Assistant to Training Children with Motor Impairments. In Proceedings of Robotica Experimental Conference (ROBOT 2011), Seville, Spain, 28-29 November 2011.