Human action recognition using salient opponent-based motion features

CRV 2010 - 7th Canadian Conference on Computer and Robot Vision

Volumn , Issue , 2010, Pages 362-369

  Shabani, Amir Hossein ^a   Zelek, John S ^a   Clausi, David A ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; BIO-INSPIRED; CLASSIFICATION ACCURACY; DATA SETS; FEATURE EXTRACTION METHODS; HUMAN-ACTION RECOGNITION; MOTION ENERGY; MOTION FEATURES; MOTION FILTERS; MULTISCALES; PERCEPTION MODEL; REGIONS OF INTEREST; SALIENT FEATURES; SPATIO TEMPORAL FILTERING;

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; GESTURE RECOGNITION;

FEATURE EXTRACTION;

EID: 77954392922     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRV.2010.54     Document Type: Conference Paper

References (26)

1. T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, pages 177-280, 2008.
2. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91-110, 2004.
3. I. Laptev, B. Caputo, C. Schuldt, and T. Lindeberg. Local velocity-adapted motion events for spatio-temporal recognition. Computer Vision and Image Understanding, pages 207-229, 2007.
4. G. Willems, T. Tuytelaars, , and L. Van Gool. An efficient dense and scale-invariant spatio-temporal interest point detector. European Conference on Computer Vision, pages 650-663, 2008.
5. K. Rapantzikos, Y. Avrithis, and S. Kollias. Dense saliency-based spatiotemporal feature points for action recognition. IEEE International Conference on Computer Vision and Pattern Recognition, pages 1454-1461, 2009.
6. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal filters. IEEE International Workshop VS-PETS, Beijing, China, pages 65-72, August 2005.
7. A. B. Watson and A. J. Ahumada. Model of human visual-motion sensing. Journal of Optical Society of America, 2(2):322-342, 1985.
8. M.J. Escobar, G.S. Masson, T. Vieville, and P. Kornprobst. Action recognition using a bio-inspired feed-forward spiking network. International Journal of Computer Vision, 82(3):284-301, 2009.
9. E. H. Adelson and J.R. Bergen. Spatio-temporal energy models for the perception of motion. Optical Society of America, pages 284-299, 1985.
10. S.J.M. Rainvillea, W.L. Makousb, and N.E. Scott- Samuelc. Opponent-motion mechanisms are self-normalizing. Vision Research, 45:1115-1127, 2005.
11. W. Reichardt. Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. in Sensory Communication,W. A. Rosenblith, Wiley, New York, 1961.
12. D.J. Heeger, G.M. Boynton, J.B. Demb, E.Seidemann, and W.T. Newsome. Motion opponency in visual cortex. Journal of Neuroscience, 19(16):7162-7174, 1999.
13. L. Zemany, C.F. Stromeyer, and A. Chaparroand R.E. Kronauer. Motion detection on flashed, stationary pedestal gratings: Evidence for an opponent-motion mechanism. Vision Research, 38:795-812, 1998.
14. P. Scovanner, S. Ali, and M. Shah. A 3-dimensional SIFT descriptor and its application to action recognition. In ACM Multimedia, pages 357-360, 2007.
15. H. Shabani, D.A. Clausi, and J.S. Zelek. Towards a robust spatio-temporal interest point detection for human action recognition. Canadian Conference on Computer and Robot Vision, pages 237-243, 2009.
16. T. Lindeberg. Scale-space: A basis for early vision. In ISRN KTH, 1993.
17. J. C. Niebles and L. Fei-Fei. A hierarchical model of shape and appearance for human action classification. IEEE International Conference on Computer Vision and Pattern Recognition, pages 299-318, 2007.
18. E. P. Simoncelli and D. J. Heeger. A model of neuronal responses in visual area mt. Vision Research, 38(5):743-761, 1997.
19. H. Jhuang, T. Serre, L. Wolf, and T. Poggio. A biologically inspired system for action recognition. IEEE International Conference on Computer Vision, pages 1-8, 2007.
20. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. IEEE International Conference on Computer Vision, pages 1395-1402, October 2005.
21. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region detectors. International Journal of Computer Vision, pages 43-72, 2006.
22. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(10):1615-1630, 2006.
23. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. Biritish Machine Vision Conference, London, UK, pages 1-11, September 2009.
24. A. Klaser, M. Marszaek, , and C. Schmid. A spatio-temporal descriptor based on 3D gradients. British Machine Vision Conference, pages 995-1004, 2008.
25. J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision, pages 299-318, 2008.
26. T. Goodhart, P. Yan, and M. Shah. Action recognition using spatio-temporal regularity based features. IEEE International Conference on Acustics, Speech and Signal Processing, pages 745-748, 2008.