Exploiting petri-net structure for activity classification and user instruction within an industrial setting

ICMI'11 - Proceedings of the 2011 ACM International Conference on Multimodal Interaction

Volumn , Issue , 2011, Pages 113-120

  Worgan, Simon F ^a   Behera, Ardhendu ^a   Cohn, Anthony G ^a   Hogg, David C ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

hidden markov model (HMM); histogram of pairwise relation (HoPR); petri net; qualitative spatial relation; workflow modeling

Indexed keywords

ACTIVITY RECOGNITION; BAYESIAN FRAMEWORKS; CLASSIFICATION ERRORS; EXPERT ANNOTATIONS; HISTOGRAM OF PAIRWISE RELATION (HOPR); INDUSTRIAL SETTINGS; MARKOV CHAIN; ON-LINE PERFORMANCE; SAFETY-CRITICAL; SECOND ORDER PROCESS; SPATIAL RELATIONS; STATE OF THE ART; STRUCTURAL INFORMATION; SUBTASKS; TEMPORAL INFORMATION; USER INTERACTION; VISUAL FEATURE EXTRACTION; WORKFLOW MODELING; WORKFLOW MODELS; WORKFLOW MONITORING;

FEATURE EXTRACTION; INTERACTIVE COMPUTER SYSTEMS;

HIDDEN MARKOV MODELS;

EID: 83455242946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2070481.2070502     Document Type: Conference Paper

References (26)

1. Vicon systems. http://www.vicon.com.
2. M. Albanese, R. Chellappa, V. Moscato, A. Picariello, V. Subrahmanian, P. Turaga, and O. Udrea. A constrained probabilistic petri net framework for human activity detection in video. IEEE Transaactions on Multimedia, 10(8):1429-1443, 2008.
3. M. Brand, N. Oliver, and A. Pentland. Coupled hidden Markov models for complex action recognition. In CVPR, pages 994-999, 1997.
4. V. Bulitko. Machine learning for time interval Petri nets. AI 2005: Advances in Artificial Intelligence, pages 959-965, 2005.
5. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. G. David Forney. The Viterbi Algorithm. Proceedings of the IEEE, 61(3):268-278, 1973.
7. B. Hartmann, C. Schauer, and N. Link. Worker behavior interpretation for flexible production. In World Academy of Science, Engineering and Technology, pages 494-502. CESSE, 2009.
8. J. Herbst. Workflow mining with InWoLvE. Computers in Industry, 53(3):245-264, 2004.
9. T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of Uncertainty in Artigicial Intelligence, pages 22-29, 1999.
10. T. Huynh and B. Schiele. Analyzing features for activity recognition. In sOc-EUSAI '05, New York, New York, USA, 2005. ACM Press.
11. Y. A. Ivanov and A. F. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000.
12. N. C. Krishnan and S. Panchanathan. Analysis of low resolution accelerometer data for continuous human activity recognition. In ICASSP '08., pages 3337-3340. IEEE, 2008.
13. P. Lukowicz, J. Ward, H. Junker, M. Stäger, G. Tröster, A. Atrash, and T. Starner. Recognizing workshop activity using body worn microphones and accelerometers. Pervasive Computing, pages 18-32, 2004.
14. K. Lyons, H. Brashear, T. Westeyn, J. Kim, and T. Starner. GART: The gesture and activity recognition toolkit. In Proc. of the 12th int. conf. on Human-computer interaction, pages 718-727. Springer-Verlag, 2007.
15. T. Murata. Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4):541-580, 1989.
16. L. Rabiner and B. Juang. An introduction to hidden markov models. ASSP Magazine, IEEE, 3(1):4-16, 1986.
17. N. Ravi, N. Dandekar, P. Mysore, and M. Littman. Activity recognition from accelerometer data. In Proc. of the Nat. Conf. on AI, volume 20. London; AAAI Press, 2005.
18. M. Sridhar, A. G. Cohn, and D. C. Hogg. From video to RCC8: Exploiting a distance based semantics to stabilise the interpretation of mereotopological relations. In COSIT, 2011.
19. T. Stiefmeier, G. Ogris, H. Junker, P. Lukowicz, and G. Troster. Combining Motion Sensors and Ultrasonic Hands Tracking for Continuous Activity Recognition in a Maintenance Scenario. 2006 10th IEEE International Symposium on Wearable Computers, 1:97-104, Oct. 2006.
20. W. M. P. van der Aalst, B. Vandongen, J. Herbst, L. Maruster, G. Schimm, and a. Weijters. Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering, 47(2):237-267, Nov. 2003.
21. G. Veres, H. Grabner, L. Middleton, and L. Van Gool. Automatic Workflow Monitoring in Industrial Environments. In Asian Conference on Computer Vision, pages 1-14, 2010.
22. X. Wang, X. Ma, and W. E. L. Grimson. Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans. PAMI, 31:539-555, 2009.
23. J. a. Ward, P. Lukowicz, G. Troster, and T. E. Starner. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE transactions on pattern analysis and machine intelligence, 28(10):1553-67, Oct. 2006.
24. T. Westeyn, H. Brashear, A. Atrash, T. Starner, and A. Drive. Georgia Tech Gesture Toolkit : Supporting Experiments in Gesture Recognition. In ICMI '03, pages 85-92, 2003.
25. S. Young. HTK: Hidden Markov Model Toolkit: Design and philosophy.
26. D. Zuehlke. Smartfactory - from vision to reality in factory technologies. In Proceedings of the 17th International Federation of Automatic Control, pages 82-89, 1997.