Spatiotemporal sparsity induced similarity measure for human action recognition

International Journal of Digital Content Technology and its Applications

Volumn 4, Issue 8, 2010, Pages

  Liu, Anan ^a   Han, Dong ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Human action recognition; l1Norm; Sparse representation; Spatiotemporal

Indexed keywords

ACTION RECOGNITION; DATA SETS; FEATURE POINT DETECTION; GENERALIZATION ABILITY; HUMAN ACTIONS; HUMAN BEHAVIOR RECOGNITION; HUMAN-ACTION RECOGNITION; L1NORM; LARGE SCALE EXPERIMENTS; LINEAR COMBINATIONS; MODEL SELECTION; MODEL-FREE METHOD; SIMILARITY MEASURE; SPARSE REPRESENTATION; SPATIO TEMPORAL FEATURES; SPATIOTEMPORAL; STATE-OF-ART METHODS; TEMPLATE MATCHING METHOD; THREE PROBLEMS; VIDEO DECOMPOSITION; VIDEO REPRESENTATIONS;

DATA PROCESSING; GESTURE RECOGNITION; TEMPLATE MATCHING;

BEHAVIORAL RESEARCH;

EID: 78651588383     PISSN: 19759339     EISSN: None     Source Type: Journal    
DOI: 10.4156/jdcta.vol4.issue8.16     Document Type: Article

References (26)

1. J. K. Aggarwal and Q. Cai. Human motion analysis: a review. IEEE Proceedings of Nonrigid and Articulated Motion Workshop, pages 90-102, 1997.
2. W. Hu, T. Tan, L. Wang, and S. Maybank. A survey on visual surveillance of object motion and behaviors. SMC
3. Schuldt, C. Laptev, and B. I. Caputo. Recognizing human actions: a local SVM approach. ICPR(17), pages 32-36,2004.
4. I. Laptev and T. Lindeberg. Space-time interest points. ICCV, pages 432-439, 2003.
5. E. Shechtman and M. Irani. Space-time behavior-based correlation - OR - How to tell if two underlying motion fields are similar without computing them?. PAMI, 29(11):2045-2056, 2007.
6. J. Liu and M. Shah. Learning human actions via information maximization. CVPR, pages 1-8, 2008.
7. I. Laptev, M. Marszałek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. CVPR, pages1-8, 2008.
8. A. Klaser, M. Marszałek, and C. Schmid. A spatio-temporal descriptor based on 3D-gradients. BMVC, pages xx-yy, 2008.
9. S.-F. Wong and R. Cipolla. Extracting spatiotemporal interest points using global information. ICCV, pages 1-8, 2007.
10. G. Willems, T. Tuytelaars, and L. V. Gool. An efficient dense and scale-invariant spatio-temporal interest point detector. ECCV, pages 650-663, 2008.
11. A. F. Bobick and J. W. Davis. The recognition of human movement using temporal templates. PAMI, 23(3):257-267, 2001.
12. L. Gorelick, M. Galun, E. Sharon, R. Basri, and A. Brandt. Shape representation and classification using the Poisson Equation. CVPR(2), pages II-61-II-67, 2004.
13. L. Wang and D. Suter. Learning and matching of dynamic shape manifolds for human action recognition. IEEE Transactions on Image Processing, 16(6):1646-1661, 2007.
14. Ming-yu Chen and Alex Hauptmann. MoSIFT: Reocgnizing Human Actions in Surveillance Videos. CMU-CS-09-161, Carnegie Mellon University, 2009.
15. L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. 2005.
16. T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV, 43(1):29-44, June 2001.
17. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009.
18. J. Yang, J. Wright, T. Huang, and Y. Ma. Image superresolution as sparse representation of raw patches. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2008.
19. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. IP, 54(12):3736-3745, December 2006.
20. L. Qiao, S. Chen, X. Tan, Sparsity Preserving Projections with Application to Face Recognition, Pattern Recognition, 2009, in press.
21. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Learning discriminative dictionaries for local image analysis. In Proc. IEEE CVPR, 2008.
22. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Adv. NIPS, volume 21, 2009.
23. D. Donoho. For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Comm. Pure and Applied Math, 59(6):797-829, 2006. 3
24. E.J. Andes, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8): 1207-1223, 2008.3.
25. J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. IJCV, 79(3):299-318, 2008.
26. S. Nowozin, G.O. Bakir, and K. Tsuda. Discriminative subsequence mining for action classification. ICCV, pages 1-8, 2007.