Human Motion Classification Based on Global Representation and Conditional Model
Abstract
Keywords
References
[1] J. K. Aggarwal and Q. Cai, “Human motion analysis: A review,” Comput. Vision Image Understanding, vol. 73, no. 3, pp. 428–440, Mar. 1999.
http://dx.doi.org/10.1006/cviu.1998.0744
[2] D. M. Gavrila, “The visual analysis of human movement: A survey,” Comput. Vision Image Understanding, vol. 73, no. 1, pp. 82–98, Jan. 1999.
http://dx.doi.org/10.1006/cviu.1998.0716
[3] T. B. Moeslund and E. Granum, “A survey of computer vision-based human motion capture,” Comput. Vision Image Understanding, vol. 81, no. 3, pp. 231–268, Mar. 2001.
http://dx.doi.org/10.1006/cviu.2000.0897
[4] T. B. Moeslund, A. Hilton, and V. Krger, “A survey of advances in vision-based human motion capture and analysis,” Comput. Vision Image Understanding, vol. 104, no. 2-3 SPEC. ISS., pp. 90–126, Nov./Dec. 2006.
[5] P. Turaga, R. Chellappa, V. S. Subrahmanian, and O. Udrea, “Machine recognition of human activities: A survey,” IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 11, pp. 1473-1488, Nov. 2008.
http://dx.doi.org/10.1109/TCSVT.2008.2005594
[6] R. Poppe, “A survey on vision-based human action recognition,” Image Vision Comput., vol. 28, no. 6, pp. 976-990, Jun. 2010.
http://dx.doi.org/10.1016/j.imavis.2009.11.014
[7] I. Laptev, “On space-time interest points,” Int. J. Comput. Vision, vol. 64, no. 2-3, pp. 107–123, Sep. 2005.
http://dx.doi.org/10.1007/s11263-005-1838-7
[8] P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features,” In Proc. 2nd Joint IEEE Int. Worksh. Visual Surveil. Perf. Evaluat. Track. Surveil., Oct. 2005.
[9] G. Willems, T. Tuytelaars, and L. J. V. Gool, “An efficient dense and scale-invariant spatio-temporal interest point detector,” Lect. Notes Comput. Sci., vol. 5303 LNCS, no. PART 2, pp. 650-663, Oct. 2008.
[10] J. C. Niebles, H. Wang, and L. Fei-Fei, “Unsupervised learning of human action categories using spatial-temporal words,” Int. J. Comput. Vision, vol. 79, no. 3, pp. 299–318, Sep. 2008.
http://dx.doi.org/10.1007/s11263-007-0122-4
[11] N. Ikizler and D. A. Forsyth, “Searching for complex human activities with no visual examples,” Int. J. Comput. Vision, vol. 80, no. 3, pp. 337–357, Dec. 2008.
http://dx.doi.org/10.1007/s11263-008-0142-8
[12] L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 12, pp. 2247–2253, Dec. 2007.
http://dx.doi.org/10.1109/TPAMI.2007.70711
PMid:17934233
[13] A. Yilmaz and M. Shah, “Actions sketch: A novel action representation,” In Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, pp. 984–989, Jun. 2005.
[14] A. Efros, A. Berg, G. Mori, and J. Malik, “Recognizing action at a distance,” In Proc. IEEE Int. Conf. Comput. Vision, pp. 726–733, Oct. 2003.
[15] D. Tran and A. Sorokin, “Human activity recognition with metric learning,” Lect. Notes Comput. Sci., vol. 5302 LNCS, no. PART 1, Oct. 2008.
[16] A. Bissacco, A. Chiuso, Y. Ma, and S. Soatto, “Recognition of human gaits,” In Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, vol. 2, pp. 52–57, Dec. 2001.
[17] A. Bissacco, A. Chiuso, and S. Soatto, “Classification and recognition of dynamical models: The role of phase, independent components, kernels and optimal transport,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no.11, pp. 1958–1972, Nov. 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1101
PMid:17848777
[18] S. Ali, A. Basharat, and M. Shah, “Chaotic invariants for human action recognition,” In Proc. IEEE Int. Conf. Comput. Vision, Oct. 2007.
[19] L. Rabiner, “A tutorial on Hidden Markov Models and selected applications in speech recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257–286, Feb. 1989.
http://dx.doi.org/10.1109/5.18626
[20] J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields: probabilistic models for segmenting and labeling sequence data,” In Internat. Conf. on Machine Learning, 2001.
[21] C. Sminchisescu, A. Kanaujia, and D. Metaxas, “Conditional models for contextual human motion recognition,” Comput. Vision Image Understanding, vol. 104, no. 2-3 SPEC. ISS., pp. 210-220, Nov./Dec. 2006.
[22] T. Huang, C. Shi, and F. Li, “Discriminative random fields for behavior modeling,” In WRI World Congr. Comput. Sci. Inf. Eng., vol. 5, pp. 17-21, Mar. 2009.
[23] H. Zhang, Z. Liu, H. Zhao, and G. Cheng, “Recognizing human activities by key frame in video sequences,” Journal of Software, vol. 5, no. 8, pp. 818-825, Aug. 2010.
[24] S. Tabbone, L. Wendling, and J.-P. Salmon, “A new shape descriptor defined on the Radon transform,” Comput. Vision Image Understanding, vol. 102, no. 1, pp. 42-51, Apr. 2006.
http://dx.doi.org/10.1016/j.cviu.2005.06.005
[25] S. R. Deans, Applications of the Radon Transform, Wiley Interscience Publications, 1983.
[26] Y. Wang, K. Huang, and T. Tan, “Human activity recognition based on transform,” Proc. Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition, pp. 3722-3729, Jun. 2007.
[27] C. Sutton and A. McCallum, An Introduction to Conditional Random Fields for Relational Learning. Introduction to Statistical Relational Learning, MIT Press, Cambridge, 2006.
[28] N. Ikizler and P. Duygulu, “Histogram of oriented rectangles: A new pose descriptor for human action recognition,” Image Vision Comput., vol. 27, no. 10, pp. 1515-1526, Sep. 2009.
http://dx.doi.org/10.1016/j.imavis.2009.02.002
Full Text: PDF


