Automatic segmentation and recognition of human activities from observation based on semantic reasoning

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2014, Pages 5043-5048

  Ramirez Amaro, Karinne ^a   Beetz, Michael ^b   Cheng, Gordon ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF BREMEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL ROBOTS; ANTHROPOMORPHIC ROBOTS; BEHAVIORAL RESEARCH; DECISION TREES; INTELLIGENT ROBOTS; SEMANTICS; TREES (MATHEMATICS);

ABSTRACT REPRESENTATION; AUTOMATIC SEGMENTATIONS; HUMAN ACTIVITIES; PERCEPTION SYSTEMS; PHYSICAL SYSTEMS; PRE-PROCESSING STEP; SEMANTIC REASONING; SEMANTIC REPRESENTATION;

SOCIAL ROBOTS;

EID: 84911474575     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2014.6943279     Document Type: Conference Paper

References (23)

1. D. Wolpert and Z. Ghahramani, "Computational principles of movement neuroscience," Nature Neuroscience Supplement, vol. 3, pp. 1212-1217, 2000.
2. L. Kunze, M. E. Dolha, and M. Beetz, "Logic programming with simulation-based temporal projection for everyday robot object manipulation." in IROS. IEEE, 2011, pp. 3172-3178.
3. W. Takano and Y. Nakamura, "Humanoid robot's autonomous acquisition of proto-symbols through motion segmentation," in Humanoids. IEEE, 2006, pp. 425-431.
4. R. Poppe, "A survey on vision-based human action recognition." Image Vision Comput., vol. 28, no. 6, pp. 976-990, 2010.
5. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng, "Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis." in CVPR. IEEE, 2011, pp. 3361-3368.
6. D. J. Patterson, D. Fox, H. A. Kautz, and M. Philipose, "Fine-Grained Activity Recognition by Aggregating Abstract Object Usage." in ISWC. IEEE Computer Society, 2005, pp. 44-51.
7. F. Wörgötter, A. Agostini, N. Krüger, N. Shylo, and B. Porr, "Cognitive agents-A procedural perspective relying on the predictability of Object-Action-Complexes (OACs)." Robotics and Autonomous Systems, vol. 57, no. 4, pp. 420-432, 2009.
8. M. Wächter, S. Schulz, T. Asfour, E. Aksoy, F. Wörgötter, and R. Dillmann, "Action Sequence Reproduction based on Automatic Segmentation and Object-Action Complexes," in IEEE/RAS International Conference on Humanoid Robots (Humanoids), 2013.
9. E. E. Aksoy, A. Abramov, J. Dörr, K. Ning, B. Dellen, and F. Wörgötter, "Learning the semantics of object-action relations by observation." I. J. Robotic Res., vol. 30, no. 10, pp. 1229-1249, 2011.
10. Y. Kuniyoshi, M. Inaba, and H. Inoue, "Learning by watching: Extracting reusable task knowledge from visual observation of human performance," IEEE Transactions on Robotics and Automation, vol. 10, no. 6, pp. 799-822, 1994.
11. A. Fern, J. M. Siskind, and R. Givan, "Learning Temporal, Relational, Force-Dynamic Event Definitions from Video." in AAAI/IAAI, R. Dechter and R. S. Sutton, Eds. AAAI Press / The MIT Press, 2002, pp. 159-166.
12. Y. Yang, C. Fermüller, and Y. Aloimonos, "Detection of Manipulation Action Consequences (MAC)." in CVPR. IEEE, 2013, pp. 2563-2570.
13. A. Guha, Y. Yang, C. Fermuuller, and Y. Aloimonos, "Minimalist plans for interpreting manipulation actions," in IROS, 2013, pp. 5908-5914.
14. H. A. Kautz, H. A. Kautz, R. N. Pelavin, J. D. Tenenberg, and M. Kaufmann, "A formal theory of plan recognition and its implementation," in Reasoning about Plans. Morgan Kaufmann, 1991, pp. 69-125.
15. R. Jäkel, S. R. Schmidt-Rohr, M. Lösch, and R. Dillmann, "Representation and constrained planning of manipulation strategies in the context of Programming by Demonstration." in ICRA. IEEE, 2010, pp. 162-169.
16. M. Beetz, M. Tenorth, D. Jain, and J. Bandouch, "Towards Automated Models of Activities of Daily Life," Technology and Disability, vol. 22, 2010.
17. S. Albrecht, K. Ramirez-Amaro, F. Ruiz-Ugalde, D. Weikersdorfer, M. Leibold, M. Ulbrich, and M. Beetz, "Imitating human reaching motions using physically inspired optimization principles." in Humanoids. IEEE, 2011, pp. 602-607.
18. M. Carpenter and J. Call, "The question of what to imitate: inferring goals and intentions from demonstrations," in Imitation and Social Learning in Robots, Humans and Animals., K. Dautenhahn and C. L. Nehaniv, Eds. MIT Press, 2007.
19. K. Ramirez-Amaro, E.-S. Kim, J. Kim, B.-T. Zhang, M. Beetz, and G. Cheng, "Enhancing Human Action Recognition through Spatiotemporal Feature Learning and Semantic Rules," in Humanoid Robots, 2013, 13th IEEE-RAS International Conference, October 2013.
20. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1993.
21. K. Ramirez-Amaro, M. Beetz, and G. Cheng, "Extracting Semantic Rules from Human Observations." in ICRA workshop: Semantics, Identification and Control of Robot-Human-Environment Interaction., May 2013.
22. G. Metta, G. Sandini, D. Vernon, L. Natale, and F. Nori, "The iCub humanoid robot: An open platform for research in embodied cognition," in PerMIS, 2008, pp. 19-21.
23. E. C. Dean-Leon, V. Parra-Vega, and A. Espinosa-Romero, "Global Uncalibrated Visual Servoing for Constrained Robots Working on an Uncalibrated Environments." in IROS. IEEE, 2006, pp. 3809-3816.