Joint action segmentation and classification by an extended hidden markov model

IEEE Signal Processing Letters

Volumn 20, Issue 12, 2013, Pages 1207-1210

  Borzeshi, Ehsan Zare ^a   Concha, Oscar Perez ^b   Xu, Richard Yi Da ^a   Piccardi, Massimo ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Action classification; Action segmentation; Hidden Markov Model; Joint segmentation and classification; Probabilistic PCA; Student's

Indexed keywords

ACTION CLASSIFICATIONS; ACTION SEGMENTATION; CLASSIFICATION APPROACH; CLASSIFICATION TASKS; HIDDEN MARKOV MODELS (HMMS); INFERENCE ALGORITHM; JOINT SEGMENTATION; PROBABILISTIC PCA;

CLASSIFICATION (OF INFORMATION); INFERENCE ENGINES; INFORMATION RETRIEVAL SYSTEMS; SPEECH RECOGNITION;

HIDDEN MARKOV MODELS;

EID: 84886686429     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2013.2284196     Document Type: Article

References (20)

1. I. Laptev and T. Lindeberg, "Space-time interest points," in Proc. ICCV, 2003, pp. 432-439.
2. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, "Learning realistic human actions from movies," in Proc. CVPR, 2008, pp. 1-8.
3. S. Maji, L. Bourdev, and J. Malik, "Action recognition from a distributed representation of pose and appearance," in Proc. CVPR, 2011, pp. 3177-3184.
4. Y. Jia and T. Darrell, "Heavy-tailed distances for gradient based image descriptors," in Proc. NIPS, 2011, pp. 397-405.
5. C. Schuldt, I. Laptev, and B. Caputo, "Recognizing human actions: A local svm approach," in Proc. ICPR, 2004, pp. 32-36.
6. A. Gilbert, J. Illingworth, and R. Bowden, "Fast realistic multi-action recognition using mined dense spatio-temporal features," in Proc. ICCV, 2009, pp. 925-931.
7. A. Kovashka and K. Grauman, "Learning a hierarchy of discriminative space-time neighborhood features for human action recognition," in Proc. CVPR, 2010, pp. 2046-2053.
8. H. Wang, M. M. Ullah, A. Kläser, I. Laptev, and C. Schmid, "Evaluation of local spatio-temporal features for action recognition," in British Machine Vision Conference, 2009, p. 127.
9. O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, "Automatic annotation of human actions in video," in Proc. ICCV, 2009, pp. 1491-1498.
10. D. L. Vail, M. M. Veloso, and J. D. Lafferty, "Conditional randomfields for activity recognition," in Proc. AAMAS '07, 2007, pp. 235:1-235:8.
11. W. Li, Z. Zhang, and Z. Liu, "Expandable data-driven graphical modeling of human actions based on salient postures," IEEE T-CSVT, vol. 18, pp. 1499-1510, 2008.
12. M. Hoai, Z. Lan, and F. De la Torre, "Joint segmentation and classification of human actions in video," in Proc. CVPR, 2011.
13. O. Concha, R. Da Xu, Z. Moghaddam, and M. Piccardi, "HMM-MIO: An enhanced hidden Markov model for action recognition," in Proc. CVPRW, 2011, pp. 62-69.
14. F. D. la Torre, J. Hodgins, J. Montano, and S. Valcarcel, Detailed human data acquisition of kitchen activities: the cmu-multimodal activity database (cmu-mmac) Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep. RI-TR-08-22h, 2008.
15. L. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, pp. 257-286, 1989.
16. M. E. Tipping and C. M. Bishop, "Probabilistic principal component analysis," J. R. Stat. Soc. Ser. B, Stat. Methodol., vol. 61, no. 3, pp. 611-622, 1999.
17. S. Chatzis, D. Kosmopoulos, and T. Varvarigou, "Robust sequential data modeling using an outlier tolerant hidden markov model," IEEE Trans. Patt. Anal. Mach. Intell., vol. 31, pp. 1657-1669, 2009.
18. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. ICML, 2001, pp. 282-289.
19. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," J. Mach. Learn. Res., vol. 6, pp. 1453-1484, 2005.
20. S. B. Wang, A. Quattoni, L.-P. Morency, and D. Demirdjian, "Hidden conditional random fields for gesture recognition," in Proc. CVPR, 2006, pp. 2:1521-2:1527.