Workflow activity monitoring using dynamics of pair-wise qualitative spatial relations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7131 LNCS, Issue , 2012, Pages 196-209

  Behera, Ardhendu ^a   Cohn, Anthony G ^a   Hogg, David C ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Activity Recognition; Hidden Markov Model (HMM); Probabilistic Latent Scemantic Analysis (pLSA); Qualitative Spatio temporal Relations; Workers Instructions

Indexed keywords

ACTIVITY RECOGNITION; HIDDEN MARKOV MODEL (HMM); PROBABILISTIC LATENT SCEMANTIC ANALYSIS (PLSA); SPATIO-TEMPORAL RELATIONS; WORKERS INSTRUCTIONS;

HIDDEN MARKOV MODELS; SEMANTICS;

DYNAMICS;

EID: 84855801175     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-27355-1_20     Document Type: Conference Paper

References (28)

1. Ivanov, Y., Bobick, A.F.: of visual acticities and interactions by stochastic parsing. IEEE Trans. on PAMI 22(8), 852-872 (2000)
2. Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832-843 (1983)
3. Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics 41(1), 164-171 (1970)
4. Blei, D.M., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022 (2003)
5. Brand, M., Oliver, N., Pentland, A.: Coupled Hidden Markov models for complex action recognition. In: Proc. of IEEE CVPR, pp. 994-999 (1997)
6. Bui, H., Venkatesh, S., West, G.: Policy recognition in the abstract hidden markov model. Journal of Artificial Intelligence Research 17, 451-499 (2002)
7. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
8. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Royal Statistical Society 39(1), 1-38 (1977)
9. Grimson, W.E.L., Stauffer, C., Romano, R., Lee, L.: Using adaptive tracking to classify and monitor activities in a site. In: Proc. of IEEE CVPR, pp. 22-29 (1998)
10. Hofmann, T.: Probabilistic latent semantic analysis. In: Proc. of Uncertainty in Artificial Intelligence, UAI 1999, pp. 289-296 (1999)
11. Hongeng, S., Nevatia, R.: Multi-agent event recognition. In: Proc. of ICCV, vol. 2, pp. 84-91 (2001)
12. Intille, S.S., Bobick, A.F.: A framework for recognizing multi-agent action from visual evidence. In: AAAI 1999, pp. 518-525 (1999)
13. Jaeger, H.: The "echo state" approach to analysing and training recurrent neural networks. Tech. rep., Fraunhofer Institute for Autonomous Intelligent Systems (December 2001)
14. Johnson, N., Hogg, D.C.: Learning the distribution of object trajectories for event recognition. Image Vision Comput. 14(8), 609-615 (1996)
15. Moore, D., Essa, I.: Recognizing multitasked activities from video using stochastic context-free grammar. In: Proc. AAAI National Conf. on AI, pp. 770-776 (2002)
16. Nguyen, N., Phung, D., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In: Proc. of IEEE CVPR, vol. 2, pp. 955-960 (2005)
17. Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Understanding 96(2), 163-180 (2004)
18. Padoy, N., Weinl, D.M.D., Berger, M.O., Navab, N.: Workflow monitoring based on 3D motion features. In: Proc. of ICCV Workshop on Video-oriented Object and Event Classification (2009)
19. Pinhanez, C., Bobick, A.: Human action detection using pnf propagation of temporal constraints. In: Proc. of IEEE CVPR (1998)
20. Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: Proc. ICPR, pp. 32-36 (2004)
21. Shi, Y., Huang, Y., Minnen, D., Bobick, A., Essa, I.: Propagation networks for recognition of partially ordered sequential action. In: Proc. of CVPR, vol. 2, pp. 862-869 (2004)
22. Veres, G., Grabner, H., Middleton, L., Van Gool, L.: Automatic Workflow Monitoring in Industrial Environments. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 200-213. Springer, Heidelberg (2011)
23. Vicon Systems, http://www.vicon.com
24. Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. on Information Theory 13(2), 260-269 (1967)
25. Vogler, C., Metaxas, D.: Parallel Hidden Markov Models for American sign language recognition. In: Proc. of IEEE ICCV, vol. 1, pp. 116-122 (1999)
26. Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. PAMI 31, 539-555 (2009)
27. Wanstall, B.: HUD on the Head for Combat Pilots. Interavia 44, 334-338 (1989)
28. Young, S.J.: The htk hidden Markov model toolkit: Design and philosophy. Tech. rep., Cambridge University Engineering Department (September 1994)