Recognizing interleaved and concurrent activities: A statistical-relational approach

2011 IEEE International Conference on Pervasive Computing and Communications, PerCom 2011

Volumn , Issue , 2011, Pages 1-9

  Helaoui, Rim ^a   Niepert, Mathias ^a   Stuckenschmidt, Heiner ^a  

^a UNIVERSITY OF MANNHEIM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY RECOGNITION; BACKGROUND KNOWLEDGE; CONCURRENT ACTIVITIES; DATA SETS; LIGHT WEIGHT; MARKOV LOGIC; RECOGNITION ACCURACY; SENSOR TECHNOLOGIES; STATE-OF-THE-ART ALGORITHMS; TRAINING DATA;

DATA PROCESSING; SENSORS;

UBIQUITOUS COMPUTING;

EID: 79957945417     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PERCOM.2011.5767586     Document Type: Conference Paper

References (28)

1. D. Hao Hu, S. J. Pan, V. W. Zheng, N. N. Liu, and Q. Yang, "Real world activity recognition with multiple goals," in Ubi-Comp '08: Proceedings of the 10th international conference on Ubiquitous computing. New York, NY, USA: ACM, 2008, pp. 30-39.
2. M. Cirillo, F. Lazellotto, F. Pecora, and A. Saffiotti, "Monitoring domestic activities with temporal constraints and components," in Proc of the 5th Int Conf on Intelligent Environments (IE09), Barcelona, ES, 2009, pp. 117-124.
3. R. Biswas, S. Thrun, and K. Fujimura, "Recognizing activities with multiple cues," in HUMO'07: Workshop on Human Motion, 2007, pp. 255-270.
4. S. D. Tran and L. S. Davis, "Event modeling and recognition using markov logic networks," in ECCV '08: Proceedings of the 10th European Conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 610-623.
5. M. Buettner, R. Prasad, M. Philipose, and D. Wetherall, "Recognizing daily activities with rfid-based sensors," in Ubicomp '09: Proceedings of the 11th international conference on Ubiquitous computing. New York, NY, USA: ACM, 2009, pp. 51-60.
6. D. J. Patterson, D. Fox, H. Kautz, and M. Philipose, "Finegrained activity recognition by aggregating abstract object usage," in ISWC '05: Proceedings of the Ninth IEEE International Symposium on Wearable Computers. Washington, DC, USA: IEEE Computer Society, 2005.
7. M. Stikic and B. Schiele, "Activity recognition from sparsely labeled data using multi-instance learning," in LoCA. Springer, 2009, pp. 156-173.
8. T. Gu, S. Chen, X. Tao, and J. Lu, "An unsupervised approach to activity recognition and segmentation based on object-use fingerprints," Data Knowledge Engineering, vol. 69, no. 6, pp. 533-544, 2010.
9. H. Jeon, T. Kim, and J. Choi, "Ontology-based user intention recognition for proactive planning of intelligent robot behavior," in MUE '08: Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering. Washington, DC, USA: IEEE Computer Society, 2008, pp. 244-248.
10. M. Richardson and P. Domingos, "Markov logic networks," Machine Learning, vol. 62, pp. 107-136, 2006.
11. R. Helaoui, "Towards a proactive system based on activity recognition," in PerCom '10: Proceedings of the Eighth Annual IEEE International Conference on Pervasive Computing and Communications, 2010.
12. E. Kim, S. Helal, and D. Cook, "Human activity recognition and pattern discovery," IEEE Pervasive Computing, vol. 9, pp. 48-53, 2010.
13. D. H. Hu and Q. Yang, "Cigar: Concurrent and interleaving goal and activity recognition," in AAAI'08:Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008, pp. 1363-1368.
14. A. McCallum and C. Sutton, "An introduction to conditional random fields for relational learning," in Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds. MIT Press, 2006.
15. J. Modayil, T. Bai, and H. Kautz, "Improving the recognition of interleaved activities," in UbiComp '08: Proceedings of the 10th international conference on Ubiquitous computing, New York, NY, USA, 2008, pp. 40-43.
16. D. Riboni and C. Bettini, "Context-aware activity recognition through a combination of ontological and statistical reasoning," in UIC '09: Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 39-53.
17. R. Helaoui, M. Niepert, and H. Stuckenschmidt, "A statistical-relational activity recognition framework for ambient assisted living systems," in Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence, ser. Advances in Soft Computing, vol. 72/2010, 2010, pp. 247-254.
18. L. Getoor and B. Taskar, Introduction to Statistical Relational Learning. MIT Press, 2007.
19. D. Koller and N. Friedman, Probabilistic Graphical Models. MIT Press, 2009.
20. F. J. Och and H. Ney, "Discriminative training and maximum entropy models for statistical machine translation," in Proceedings of the 40th Meeting of the ACL, 2002.
21. I. Meza-Ruiz and S. Riedel, "Multilingual semantic role labelling with markov logic," in Proceedings of the Conference on Computational Natural Language Learning, 2009, pp. 85-90.
22. M. Niepert, C. Meilicke, and H. Stuckenschmidt, "A Probabilistic-Logical Framework for Ontology Matching," in Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010.
23. N. Creignou, S. Khanna, and M. Sudan, Complexity classifications of boolean constraint satisfaction problems. SIAM, 2001.
24. D. Roth and W. Yih, "Integer linear programming inference for conditional random fields," in Proceedings of the International Conference on Machine Learning, 2005, pp. 736-743.
25. B. Taskar, "Learning structured prediction models: A largemargin approach," Ph.D. dissertation, Stanford University, 2004.
26. S. Riedel, "Improving the accuracy and efficiency of map inference for markov logic," in Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2008, pp. 468-475.
27. M. Niepert, "A delayed column generation strategy for exact k-bounded map inference in markov logic networks," in Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2010.
28. A. Schrijver, Theory of Linear and Integer Programming. Wiley & Sons, 1998.