Efficient nonlinear Markov models for human motion

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1314-1321

  Lehrmann, Andreas M ^a   Gehler, Peter V ^a   Nowozin, Sebastian ^b  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

action recognition; efficient; human motion; Markov model; motion completion; nonlinear

Indexed keywords

BAYESIAN NETWORKS; MARKOV CHAINS; MONTE CARLO METHODS; PATTERN RECOGNITION;

ACTION RECOGNITION; EFFICIENT; HUMAN MOTIONS; MARKOV MODEL; NONLINEAR;

HIDDEN MARKOV MODELS;

EID: 84911409247     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.171     Document Type: Conference Paper

References (31)

1. D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, and J. Davis. SCAPE: Shape completion and animation of people. SIGGRAPH, 2005.
2. D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
3. M. Brand, N. Oliver, and A. Pentland. Coupled hidden Markov models for complex action recognition. CVPR, 1997.
4. C. Bregler. Learning and recognizing human dynamic in video sequences. CVPR, 1997.
5. L. Breiman. Bagging predictors. Mach. Learn., 1996.
6. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, 1984.
7. O. Cappé, S. J. Godsill, and E. Moulines. An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 2007.
8. O. Cappé, E. Moulines, and T. Rydéen. Inference in Hidden Markov Models. Springer, 2005.
9. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011.
10. A. Criminisi, J. Shotton, and E. Konukoglu. Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends. Comp. Graphics and Vision, 2012.
11. J. Fan and Q. Yao. Nonlinear Time Series: Nonparametric and Parametric Methods. Springer, 2005.
12. S. Fothergill, H. M. Mentis, P. Kohli, and S. Nowozin. Instructing people for training gestural interactive systems. CHI, 2012.
13. E. Fox, E. Sudderth, M. Jordan, and A. Willsky. Nonpara-metric Bayesian learning of switching linear dynamical systems. NIPS, 2008.
14. A. M. Fraser. Hidden Markov Models and Dynamical Systems. SIAM, 2008.
15. N. Gordon, D. Salmond, and A. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F. Radar and Signal Proc., 1993.
16. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 2002.
17. A. Hoerl and R. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 1970.
18. K. Ito and K. Xiong. Gaussian filters for nonlinear filtering problems. IEEE Trans. on Automatic Control, 2000.
19. S. J. Julier and J. K. Uhlmann. A new extension of the Kalman filter to nonlinear systems. Proc. AeroSense: 11th Int. Symp. Aerospace/Defense Sensing, Simulation and Controls, 1997.
20. C. Meek, D. M. Chickering, and D. Heckerman. Autoregressive tree models for time-series analysis. SIAM, 2002.
21. S. Nowozin and J. Shotton. Action points: A representation for low-latency online human action recognition. Technical report, 2012. MSR-TR-2012-68.
22. V. Pavlović, J. M. Rehg, and J. MacCormick. Learning switching linear models of human motion. NIPS, 2000.
23. S. Salvador and P. Chan. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal., 2007.
24. J. Schaefer and K. Strimmer. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Molec. Biol., 2005.
25. L. Sigal, A. Balan, and M. J. Black. HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. IJCV, 2010.
26. G. W. Taylor, G. E. Hinton, and S. Roweis. Modeling human motion using binary latent variables. NIPS, 2007.
27. R. Urtasun, D. J. Fleet, and P. Fua. 3D people tracking with Gaussian process dynamical models. CVPR, 2006.
28. R. Urtasun, D. J. Fleet, and N. D. Lawrence. Modeling human locomotion with topologically constrained latent variable models. 2nd Workshop on Human Motion, 2007.
29. J. M. Wang, D. J. Fleet, and A. Hertzmann. Gaussian process dynamical models for human motion. PAMI, 2008.
30. J. M. Wang, D. J. Fleet, and A. Hertzmann. Optimizing walking controllers for uncertain inputs and environments. ACM Trans. Graphics, 2010.
31. J. M. Wang, S. R. Hamner, S. L. Delp, and V. Koltun. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Trans. Graphics, 2012.