A selective spatio-temporal interest point detector for human action recognition in complex scenes

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1776-1783

  Chakraborty, Bhaskar ^a   Holte, Michael B ^b   Moeslund, Thomas B ^b   Gonzàlez, Jordi ^a   Xavier Roca, F ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; COMPLEX SCENES; COMPRESSION TECHNIQUES; DATA SETS; HUMAN ACTIONS; HUMAN ACTOR; HUMAN-ACTION RECOGNITION; INTEREST POINTS; RECENT PROGRESS; RECOGNITION STRATEGIES; SPATIO-TEMPORAL; STATE-OF-THE-ART PERFORMANCE; SURROUND SUPPRESSION; TEMPORAL CONSTRAINTS; VOCABULARY BUILDING;

GESTURE RECOGNITION; IMAGE PROCESSING; SUPPORT VECTOR MACHINES;

BENCHMARKING;

EID: 84856635898     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126443     Document Type: Conference Paper

References (32)

1. M. Bregonzio, S. Gong, and T. Xiang. Recognising action as clouds of space-time interest points. In CVPR, 2009. 2
2. M. Bregonzio, J. Li, S. Gong, and T. Xiang. Discriminative topics modelling for action feature selection and recognition. In BMVC, pages 111, 2010. 2, 6
3. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines, 2001. 5
4. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In VS-PETS, 2005. 2, 6, 7
5. A. Gilbert, J. Illingworth, and R. Bowden. Fast realistic multi-action recognition using mined dense spatio-temporal features. In ICCV, 2009. 1, 2, 6, 7
6. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. PAM1, 29(12):2247-2253, 2007. 1, 5, 6
7. C. Grigorescu, N. Petkov, and M. A. Westenberg. Contour and boundary detection improved by surround suppression of texture edges. IVC, 22(8):609-622, 2004. 3
8. D. Han, L. Bo, and C. Sminchisescu. Selection and context for action recognition. In ICCV, 2009. 7
9. C. Harris and M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, 1988. 2
10. N. Ikizler and D. Forsyth. Searching video for complex activities with finite state models. In CVPR, 2007. 1
11. H. Jhuang, T. Serre, L. Wolf, and T. Poggio. A biologically inspired system for action recognition. In ICCV, 2007. 2, 6
12. I.-K. Jung and S. Lacroix. A robust interest point matching algorithm. In ICCV, 2001. 3
13. Y. Ke, R. Sukthankar, and M. Hebert. Event detection in crowded videos. In ICCV, 2007. 1
14. T. Kim, S. Wong, and R. Cipolla. Tensor canonical correlation analysis for action classification. In CVPR, 2007. 1, 6
15. J. Koenderink and A. V. Doorn. Representation of local geometry in the visual system. Biological Cybernetics, 55:367-375, 1987. 4
16. I. Laptev, B. Caputo, C. Schüldt, and T. Lindeberg. Local velocity-adapted motion events for spatio-temporal recognition. IJCV, 108(3):207-229, 2007. 4, 6
17. I. Laptev and T. Lindeberg. Space-time interest points. In ICCV, 2003. 1, 2, 7
18. Z. Lin, Z. Jiang, and L. Davis. Recognizing actions by shapemotion prototype trees. In ICCV, 2009. 1, 6
19. T. Lindeberg. Feature detection with automatic scale selection. IJCV, 30(2):79-116, 1998. 2, 3
20. J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos "in the wild" In CVPR, 2009. 1, 2, 4, 5, 6
21. J. Liu and M. Shah. Learning human actions via information maximization. In CVPR, 2008. 2, 4, 6
22. V. Mahadevan and N. Vasconcelos. Spatiotemporal saliency in dynamic scenes. PAMI, 32(1):171-177, 2010. 3
23. M. Marszalek, I. Laptev, and C. Schmid. Actions in context. In CVPR, 2009. 1, 2, 4, 5, 7
24. T. Moeslund, A. Hilton, and V. Krüger. A survey of advances in vision-based human motion capture and analysis. CVIU, 104(2-3):90-126, 2006. 1
25. R. Poppe. A survey on vision-based human action recognition. IVC, 28(6):976-990, 2010. 1
26. C. Schüldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, 2004. KTH dataset available at http://www.nada.kth. se/cvap/actions. 1, 5, 6
27. N. Slonim and N. Tishby. Agglomerative information bottleneck. In NIPS, 1999. 4
28. M. Ullah, S. Parizi, and I. Laptev. Improving bag-of-features action recognition with non-local cues. In BMVC, 2010. 2, 7
29. H. Wang, M. Ullah, A. Kläser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. In BMVC, 2009. 7
30. G.Willems, T. Tuytelaars, and L. V. Gool. An efficient dense and scale-invariant spatio-temporal interest point detector. In ECCV, 2008. 2, 6
31. S. Wong and R. Cipolla. Extracting spatiotemporal interest points using global information. In ICCV, 2007. 2, 6
32. X. Wu, W. Liang, and Y. Jia. Incremental discriminativeanalysis of canonical correlations for action recognition. In ICCV, 2009. 1, 6