Articulated non-rigid point set registration for human pose estimation from 3D sensors

Sensors (Switzerland)

Volumn 15, Issue 7, 2015, Pages 15218-15245

  Ge, Song ^a   Fan, Guoliang ^a  

^a Oklahoma State University   (United States)

Author keywords

Human pose estimation; Point set registration; Segment volume validation; Visible points extraction

Indexed keywords

EXTRACTION; GEOMETRY;

3D HUMAN POSE ESTIMATION; ARTICULATED DEFORMATION; HUMAN POSE ESTIMATIONS; POINT-SET REGISTRATIONS; REGISTRATION TECHNIQUES; SEGMENT VOLUME VALIDATION; STATE-OF-THE-ART ALGORITHMS; TECHNICAL CHALLENGES;

MOTION ESTIMATION;

EID: 84934779604     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150715218     Document Type: Article

References (50)

1. Perez-Sala, X.; Escalera, S.; Angulo, C.; Gonzalez, J. A survey on model based approaches for 2D and 3D visual human pose recovery. Sensors 2014, 14, 4189-4210.
2. Escalera, S. Human behavior analysis from depth maps. In Proceedings of the International Conference on Articulated Motion and Deformable Objects, Mallorca, Spain, 11-13 July 2012; pp. 282-292.
3. Ye, M.; Zhang, Q.; Wang, L.; Zhu, J.; Yang, R.; Gall, J. A survey on human motion analysis from depth data. In Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, Grzegorzek, M.; Theobalt, C.; Koch, R.; Kolb, A., Eds.; Springer-Verlag: Berlin, Germany, 2013; pp. 149-187.
4. Xbox One. Avaliable online:http://www.xbox.com/en-US/xbox-one (accessed on 8 May 2015).
5. Sanmartín, G.; Flores, J.; Arias, P.; Cudeiro, J.; Méndez, R. Motion capture for clinical purposes, an approach using primesense sensors. In Proceedings of International Conference on Articulated Motion and Deformable Objects, Mallorca, Spain, 11-13 July 2012; pp. 273-281.
6. Gasparrini, S.; Cippitelli, E.; Spinsante, S.; Gambi, E. A depth-based fall detection system using a kinect sensor. Sensors 2014, 14, 2756-2775.
7. Jalal, A.; Kamal, S.; Kim, D. A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 2014, 14, 11735-11759.
8. Plantard, P.; Auvinet, E.; Pierres, A.L.; Multon, F. Pose estimation with a kinect for ergonomic studies: Evaluation of the accuracy using a virtual mannequin. Sensors 2015, 15, 1785-1803.
9. Moeslund, T.B.; Hilton, A.; Kruger, V. A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 2006, 104, 90-126.
10. Elgammal, A.; Lee, C.S. Inferring 3D body pose from silhouettes using activity manifold learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 27 June-2 July 2004; pp. 681-688.
11. Anguelov, D.; Srinivasan, P.; Koller, D.; Thrun, S.; Rodgers, J.; Davis, J. SCAPE: Shape completion and animation of people. ACM Trans. Gr. 2005, 24, 408-416.
12. Shotton, J.; Fitzgibbon, A.; Cook, M.; Sharp, T.; Finocchio, M.; Moore, R.; Kipman, A.; Blake, A. Real-time human pose recognition in parts from single depth images. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA, 20-25 June 2011; pp. 1297-1304.
13. Taylor, J.; Shotton, J.; Sharp, T.; Fitzgibbon, A.W. The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16-21 June 2012; pp. 103-110.
14. Burke, M.; Lasenby, J. Fast upper body joint tracking using kinect pose priors. In Proceedings of the International Conference on Articulated Motion and Deformable Objects, Palma de Mallorca, Spain, 16-18 July 2014; pp. 94-105.
15. Demirdjian, D. Combining geometric- and view-based approaches for articulated pose estimation. In Proceedings of European Conference on Computer Vision, Prague, Czech Republic, 11-14 May 2004; pp. 183-194.
16. Pellegrini, S.; Schindler, K.; Nardi, D. A generalization of the ICP algorithm for articulated bodies. In Proceedings of British Machine Vision Conference, Leeds, UK, 1-4 September 2008; pp. 87.1-87.10.
17. Horaud, R.; Forbes, F.; Yguel, M.; Dewaele, G.; Zhang, J. Rigid and articulated point registration with expectation conditional maximization. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 587-602.
18. Ye, M.; Yang, R. Real-time simultaneous pose and shape estimation for articulated objects using a single depth camera. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23-28 June 2014; pp. 2353-2360.
19. Baak, A.; Müller, M.; Bharaj, G.; Seidel, H.; Theobalt, C. A data-driven approach for real-time full body pose reconstruction from a depth camera. In Proceedings of IEEE International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011; pp. 1092-1099.
20. Helten, T.; Baak, A.; Bharaj, G.; Mãijller, M.; Seidel, H.; Theobalt, C. Personalization and evaluation of a real-time depth-based full body tracker. In Proceedings of International Conference on 3D Vision, Seattle, WA, USA, 29 June-1 July 2013; pp. 279-286.
21. Stoll, C.; Hasler, N.; Gall, J.; Seidel, H.; Theobalt, C. Fast articulated motion tracking using a sums of Gaussians body model. In Proceedings of IEEE International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011; pp. 951-958.
22. Ye, M.; Wang, X.; Yang, R.; Liu, R.; Pollefeys, M. Accurate 3D pose estimation from a single depth image. In Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011; pp. 731-738.
23. Ge, S.; Fan, G.; Ding, M. Non-rigid point set registration with global-local topology preservation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 23-28 June 2014; pp. 245-251.
24. Besl, P.J.; McKay, N.D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 14, 239-256.
25. Zhang, Z. Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 1994, 13, 119-152.
26. Zheng, Y.; Doermann, D. Robust point matching for nonrigid shapes by preserving local neighborhood structures. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 643-649.
27. Ma, J.; Zhao, J.; Tian, J.; Tu, Z.; Yuille, A.L. Robust esitimation of nonrigid transformation for point set registration. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 23-28 June 2013; pp. 2147-2154.
28. Ma, J.; Zhao, J.; Tian, J.W.; Yuille, A.L.; Tu, Z.W. Robust point matching via vector field consensus. IEEE Trans. Image Process. 2014, 23, 1706-1721.
29. Yang, J.; Williams, J.P.; Sun, Y.; Blum, R.S.; Xu, C. A robust hybrid method for nonrigid image registration. Pattern Recognit. 2011, 44, 764-776.
30. Tang, J.; Shao, L.; Zhen, X. Robust point pattern matching based on spectral context. Pattern Recognit. 2014, 47, 1469-1484.
31. Chui, H.; Rangarajan, A. A feature registration framework using mixture models. In Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Hilton Head Island, SC, USA, 11-12 June 2000; pp. 190-197.
32. Chui, H.; Rangarajan, A. A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 2003, 89, 114-141.
33. Myronenko, A.; Song, X.; Carreira-Perpinan, M.A. Non-rigid point set registration: Coherent point drift (CPD). In Proceedings of Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 4-7 December 2006; pp. 1009-1016.
34. Myronenko, A.; Song, X. Point set registration: Coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 2262-2275.
35. Jian, B.; Vemuri, B.C. A robust algorithm for point set registration using mixture of Gaussians. In Proceedings of IEEE International Conference on Computer Vision, Las Vegas, NV, USA, 17-21 October 2005; pp. 1246-1251.
36. Jian, B.; Vemuri, B.C. Robust point set registration using Gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 1633-1645.
37. Panaganti, V.; Aravind, R. Robust nonrigid point set registration using Graph-Laplacian regularization. In Proceedings of IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 5-9 January 2015; pp. 1137-1144.
38. De Sousa, S.; Kropatsch, W.G. Graph-based point drift: Graph centrality on the registration of point-sets. Pattern Recognit. 2015, 48, 368-379.
39. Droeschel, D.; Behnke, S. 3D body pose estimation using an adaptive person model for articulated ICP. In Proceedings of International Conference on Intelligent Robotics and Applications, Aachen, Germany, 6-9 December 2011; pp. 157-167.
40. Zhu, Y.; Fujimura, K. A bayesian framework for human body pose tracking from depth image sequences. Sensors 2010, 10, 5280-5293.
41. Gall, J.; Stoll, C.; de Aguiar, E.; Theobalt, C.; Rosenhahn, B.; Seidel, H.P. Motion capture using joint skeleton tracking and surface estimation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June 2009; pp. 1746-1753.
42. Calderita, L.V.; Bandera, J.P.; Bustos, P.; Skiadopoulos, A. Model-based reinforcement of kinect depth data for human motion capture applications. Sensors 2013, 13, 8835-8855.
43. Katz, S.; Tal, A.; Basri, R. Direct visibility of point sets. ACM Trans. Graph. 2007, 26, 24.
44. Katz, S.; Leifman, G.; Tal, A. Mesh segmentation using feature point and core extraction. Vis. Comput. 2005, 21, 649-658.
45. Roweis, S.T.; Saul, L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290, 2323-2326.
46. Bishop, C.M. Neural Networks for Pattern Recognition; Oxford University Press Inc.: New York, NY, USA, 1995.
47. O'Rourke, J. Finding minimal enclosing boxes. Parallel Program. 1985, 14, 183-199.
48. Chang, C.; Gorissen, B.; Melchior, S. Fast oriented bounding box optimization on the rotation group SO(3,R). ACM Trans. Graph. 2011, 30, 122.1-122.16.
49. Ge, S.; Fan, G. Non-rigid articulated point set registration for human pose estimation. In Proceedings of IEEEWinter Conference on Applications of Computer Vision,Waikoloa, HI, USA, 5-9 January 2015; pp. 94-101.
50. Ganapathi, V.; Plagemann, C.; Thrun, S.; Koller, D. Real time motion capture using a single time-of-flight camera. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13-18 June 2010; pp. 755-762.