Robust action recognition based on a hierarchical model

Proceedings - 2013 International Conference on Cyberworlds, CW 2013

Volumn , Issue , 2013, Pages 191-198

  Jiang, Xinbo ^a   Zhong, Fan ^a   Peng, Qunsheng ^b   Qin, Xueying ^a  

^a SHANDONG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Bottom up approach; Feature weighting; Hierarchical model; Robust action recognition

Indexed keywords

HIERARCHICAL SYSTEMS; MOTION ESTIMATION; MUSCULOSKELETAL SYSTEM; THREE DIMENSIONAL;

ACTION RECOGNITION; BOTTOM UP APPROACH; FEATURE WEIGHTING; HIERARCHICAL MODEL; HUMAN MACHINE INTERACTION; STATE-OF-THE-ART APPROACH; TEMPORAL DYNAMICS; VIDEO-BASED APPROACH;

IMAGE RECOGNITION;

EID: 84893325138     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CW.2013.37     Document Type: Conference Paper

References (13)

1. P. Matikainen, M. Hebert, and R. Sukthankar, "Trajectons: Action recognition through the motion analysis of tracked features," in Workshop on Video-Oriented Object and Event Classification, ICCV 2009, October 2009.
2. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for action recognition with depth cameras," in CVPR'12, 2012, pp. 1290-1297.
3. I. Laptev, "On space-time interest points," Int. J. Comput. Vision, vol. 64, no. 2-3, pp. 107-123, Sep. 2005.
4. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, "Actions as space-time shapes," in The Tenth IEEE International Conference on Computer Vision (ICCV'05), 2005, pp. 1395-1402.
5. A. Kl̈aser, M. Marsza?ek, and C. Schmid, "A spatio-temporal descriptor based on 3d-gradients," in British Machine Vision Conference, sep 2008, pp. 995-1004.
6. G. Willems, T. Tuytelaars, and L. Gool, "An efficient dense and scale-invariant spatio-temporal interest point detector," in Proceedings of the 10th European Conference on Computer Vision: Part II, ser. ECCV '08. Berlin, Heidelberg: Springer-Verlag, 2008, pp. 650-663.
7. I. Laptev, M. Marsza?ek, C. Schmid, and B. Rozenfeld, "Learning realistic human actions from movies," in Conference on Computer Vision &Pattern Recognition, jun 2008.
8. 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 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, ser. CVPR '11. Washington, DC, USA: IEEE Computer Society, 2011, pp. 1297-1304.
9. C. Ellis, S. Z. Masood, M. F. Tappen, J. J. Laviola, Jr., and R. Sukthankar, "Exploring the trade-off between accuracy and observational latency in action recognition," Int. J. Comput. Vision, vol. 101, no. 3, pp. 420-436, Feb. 2013.
10. S. Fothergill, H. M. Mentis, P. Kohli, and S. Nowozin, "Instructing people for training gestural interactive systems," in CHI, J. A. Konstan, E. H. Chi, and K. Ḧöok, Eds. ACM, 2012, pp. 1737-1746.
11. X. Yang, C. Zhang, and Y. Tian, "Recognizing actions using depth motion maps-based histograms of oriented gradients," in Proceedings of the 20th ACM international conference on Multimedia, ser. MM '12. New York, NY, USA: ACM, 2012, pp. 1057-1060.
12. F. Negin, F. Ozdemir, C. B. Akgul, K. A. Yuksel, and A. Ercil, "A decision forest based feature selection framework for action recognition from rgb-depth cameras." 2013.
13. S. P. Chatzis, D. I. Kosmopoulos, and P. Doliotis, "A conditional random field-based model for joint sequence segmentation and classification," Pattern Recognition, vol. 46, no. 6, pp. 1569-1578, 2013.