Human Activity Recognition Process Using 3-D Posture Data

IEEE Transactions on Human-Machine Systems

Volumn 45, Issue 5, 2015, Pages 586-597

  Gaglio, Salvatore ^a,b   Re, Giuseppe Lo ^a   Morana, Marco ^a  

^a UNIVERSITY OF PALERMO   (Italy)

^b CNR   (Italy)

Author keywords

Human activity recognition; kinect

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER AIDED DESIGN; IMAGE FUSION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; PATTERN RECOGNITION;

ACTIVITY RECOGNITION; HUMAN ACTIVITY RECOGNITION; K-MEANS CLUSTERING; KINECT; MACHINE LEARNING TECHNIQUES; MARKOV RANDOM FIELDS; MAXIMUM ENTROPY MARKOV MODEL; SPATIOTEMPORAL EVOLUTION;

HIDDEN MARKOV MODELS;

EID: 84942240225     PISSN: 21682291     EISSN: None     Source Type: Journal    
DOI: 10.1109/THMS.2014.2377111     Document Type: Article

References (38)

1. J. Yamato, J. Ohya, and K. Ishii, "Recognizing human action in time-sequential images using hidden Markov model, " in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog. Proc., 1992, pp. 379-385.
2. M. P. V. Kellokumpu and J. Heikkila, "Human activity recognition using sequences of postures, " in Proc. IAPR Conf. Mach. Vision Appl., 2005, pp. 570-573.
3. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2001.
4. A. Oikonomopoulos, I. Patras, and M. Pantic, "Spatiotemporal salient points for visual recognition of human actions, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 3, pp. 710-719, Jun. 2005.
5. G. Willems, T. Tuytelaars, and L. Gool, "An efficient dense and scaleinvariant spatio-Temporal interest point detector, " in Proc. 10th Eur. Conf. Comput. Vision, 2008, pp. 650-663.
6. S. J. Preece, J. Y. Goulermas, L. P. J. Kenney, D. Howard, K. Meijer, and R. Crompton, "Activity identification using body-mounted sensors-A review of classification techniques, " Physiol. Meas., vol. 30, no. 4, pp. R1-R33, 2009.
7. L. Bao and S. S. Intille, "Activity recognition from user-Annotated acceleration data, " in Pervasive Computing (ser. Lecture Notes in Computer Science, vol. 3001), A. Ferscha and F. Mattern, Eds. Berlin, Germany: Springer, 2004, pp. 1-17.
8. P. Cottone, G. Lo Re, G. Maida, and M. Morana, "Motion sensors for activity recognition in an ambient-intelligence scenario, " in Proc. PerCom Workshops, 2013, pp. 646-651.
9. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Learning actionlet ensemble for 3D human action recognition, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 5, pp. 914-927, May 2014.
10. W. Li, Z. Zhang, and Z. Liu, "Action recognition based on a bag of 3D points, " in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog. Workshops, 2010, pp. 9-14.
11. L. Xia, C.-C. Chen, and J. Aggarwal, "View invariant human action recognition using histograms of 3D joints, " in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recog. Workshops, 2012, pp. 20-27.
12. L. Miranda, T. Vieira, D. Martnez, T. Lewiner, A. W. Vieira, and M. F. M. Campos, "Online gesture recognition from pose kernel learning and decision forests, " Pattern Recog. Lett., vol. 39, no. 0, pp. 65-73, 2014.
13. H. Shum, E. Ho, Y. Jiang, and S. Takagi, "Real-Time posture reconstruction for microsoft kinect, " IEEE Trans. Cybern., vol. 43, no. 5, pp. 1357-1369, Oct. 2013.
14. R. Gupta, A. Y.-S. Chia, and D. Rajan, "Human activities recognition using depth images, " in Proc. 21st ACM Int. Conf. Multimedia, 2013, pp. 283-292.
15. J. Sung, C. Ponce, B. Selman, and A. Saxena, "Unstructured human activity detection from RGBD images, " in Proc. IEEE Int. Conf. Robot. Autom., 2012, pp. 842-849.
16. H. S. Koppula, R. Gupta, and A. Saxena, "Learning human activities and object affordances from RGB-D videos, " Int. J. Rob. Res., vol. 32, no. 8, pp. 951-970, Jul. 2013.
17. B. Ni, Y. Pei, P. Moulin, and S. Yan, "Multilevel depth and image fusion for human activity detection, " IEEE Trans. Cybern., vol. 43, no. 5, pp. 1383-1394, Oct. 2013.
18. B. Ni, G. Wang, and P. Moulin, "RGBD-HuDaAct: A color-depth video database for human daily activity recognition, " in Proc. IEEE Int. Conf. Comput. Vision Workshops, Nov. 2011, pp. 1147-1153.
19. S. Fothergill, H. M. Mentis, P. Kohli, and S. Nowozin, "Instructing people for training gestural interactive systems, " in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2012, pp. 1737-1746.
20. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for action recognition with depth cameras, " in Proc. IEEE Conf. Comput. Vision Pattern Recog., Jun. 2012, pp. 1290-1297.
21. C. Wolf, J. Mille, E. Lombardi, O. Celiktutan, M. Jiu, M. Baccouche, E. Dellandŕea, C.-E. Bichot, C. Garcia, and B. Sankur, "The LIRIS human activities dataset and the ICPR 2012 human activities recognition and localization competition, " Laboratoire dInformatique en Images et Systmes dInformation, INSA de Lyon, Lyon, France, Tech. Rep. LIRIS RR-2012-004, 2012.
22. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, "Berkeley MHAD: A comprehensive multimodal human action database, " in Proc. IEEE Workshop Appl. Comput. Vision, Jan. 2013, pp. 53-60.
23. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification, " in Proc. IEEE 12th Int. Conf. Comput. Vision, Sep. 2009, pp. 221-228.
24. (2012). [Online].Available: http://pr.cs.cornell.edu/humanactivities/results. php
25. L. Chen, H. Wei, and J. Ferryman, "ReadingAct RGB-D action dataset and human action recognition from local features, " Pattern Recog. Lett., vol 50, pp. 159-169, 2014.
26. A. A. Chaaraoui, J. R. Padilla-Lopez, P. Climent-Perez, and F. Florez-Revuelta, "Evolutionary joint selection to improve human action recognition with RGB-D devices, " Expert Syst. Appl., vol. 41, no. 3, pp. 786-794, 2014.
27. C.-W. Hsu and C.-J. Lin, "A comparison of methods formulticlass support vector machines, " IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
28. K.-B. Duan and S. Keerthi, "Which is the best multiclass SVM method? An empirical study, " in Proc. 6th Int. Conf. Multiple Classifier Syst., N. Oza, R. Polikar, J. Kittler, and F. Roli, Eds. Berlin, Germany: Springer, 2005, pp. 278-285.
29. L. Rabiner, "A tutorial on hiddenMarkovmodels and selected applications in speech recognition, " Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
30. S. S. Rao, Engineering Optimization: Theory and Practice, 3rd ed. New York, NY, USA: Wiley-Interscience, 1996.
31. S. Arlot and A. Celisse, "A survey of cross-validation procedures for model selection, " Statist. Surveys, vol. 4, pp. 40-79, 2010.
32. A. De Paola, G. Lo Re, M. Morana, and M. Ortolani, "An intelligent system for energy efficiency in a complex of buildings, " in Proc. Sustainable Internet ICT Sustainability, Oct. 2012, pp. 1-5.
33. A. De Paola, M. Ortolani, G. Lo Re, G . Anastasi, and S.K. Das, "Intelligent management systems for energy efficiency in buildings: A survey, " ACM Comput. Surv., vol. 47, no. 1, pp. 13:1-13:38, Jun. 2014.
34. G. Lo Re, M. Morana, and M. Ortolani, "Improving user experience via motion sensors in an ambient intelligence scenario, " in Proc. Pervasive Embedded Comput. Commun. Syst., 2013, pp. 29-34.
35. A. D. Paola, M. L. Cascia, G. L. Re, M. Morana, and M. Ortolani, "Mimicking biological mechanisms for sensory information fusion, " Biol. Inspired Cognitive Architect., vol. 3, pp. 27-38, 2013.
36. (2014).[Online]. Available: http://www.dicgim.unipa.it/networks/ndslab/KARD/
37. X. Yang and Y. Tian, "Effective 3D action recognition using eigenjoints, " J. Visual Commun. Image Representation, vol. 25, no. 1, pp. 2-11, 2014.
38. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R.Moore, A. Kipman, and A. Blake, "Real-Time human pose recognition in parts from single depth images, " in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2011, pp. 1297-1304.