Human action recognition with salient trajectories

Signal Processing

Volumn 93, Issue 11, 2013, Pages 2932-2941

  Yi, Yang ^a   Lin, Yikun ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Human action recognition; Kernel histogram; Salient trajectory

Indexed keywords

ACTION RECOGNITION; BAG OF WORDS; HUMAN ACTIONS; HUMAN-ACTION RECOGNITION; KERNEL HISTOGRAM; MOTION SALIENCIES; STATE-OF-THE-ART METHODS; VIDEO SEQUENCES;

GESTURE RECOGNITION; GRAPHIC METHODS; IMAGE RECOGNITION; MOTION ESTIMATION; STATISTICAL METHODS;

TRAJECTORIES;

EID: 84879298813     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2013.05.002     Document Type: Article

References (48)

1. S. Sadanand, J. Corso, Action bank: a high-level representation of activity in video, in: IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1234-1241.
2. I. Laptev, T. Lindeberg, Space-time interest points, in: International Conference on Computer Vision, 2003, pp. 432-439.
3. P. Dollár, V. Rabaud, G. Cottrell, S. Belongie, Behavior recognition via sparse spatio-temporal features, in: International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, pp. 65-72.
4. R. Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: International Conference on Image Processing, 2010, pp. 2653-2656.
5. B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in: International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
6. K. Rapantzikos, Y. Avrithis, S. Kollias, Dense saliency-based spatiotemporal feature points for action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1454-1461.
7. C. Liu, and P.C. Yuen Human action recognition using boosted EigenActions Image and Vision Computing 28 5 2010 825 835
8. L. Wang, D. Zhao, Recognizing actions using salient features, in: International Workshop on Multimedia Signal Processing, 2011, pp. 1-6.
9. J. Shi, C. Tomasi, Good features to track, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp. 593-600.
10. R. Poppe A survey on vision-based human action recognition Image and Vision Computing 28 6 2010 976 990
11. D. Weinland, R. Ronfard, and E. Boyer A survey of vision-based methods for action representation, segmentation and recognition Computer Vision and Image Understanding 115 2 2011 224 241
12. J.K. Aggarwal, and M.S. Ryoo Human activity analysis a review ACM Computing Surveys 43 3 2011 1 43
13. A.F. Bobick, and J.W. Davis The recognition of human movement using temporal templates IEEE Transactions on Pattern Analysis and Machine Intelligence 23 3 2001 257 267
14. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri Actions as space-time shapes IEEE Transactions on Pattern Analysis and Machine Intelligence 29 12 2007 2247 2253
15. A. Roy, S. Sural, and J. Mukherjee Gait recognition using pose kinematics and pose energy image Signal Processing 92 3 2012 780 792
16. A.A. Efros, A.C. Berg, G. Mori, J. Malik, Recognizing action at a distance, in: International Conference on Computer Vision, 2003, pp. 726-733.
17. A. Oikonomopoulos, I. Patras, and M. Pantic Spatiotemporal salient points for visual recognition of human actions IEEE Transactions on Systems, Man, and Cybernetics, Part B Cybernetics 36 3 2006 710 719
18. G. Willems, T. Tuytelaars, L.J.V. Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in: European Conference on Computer Vision, 2008, pp. 650-663.
19. W.-T. Lee, H.-T. Chen, Histogram-based interest point detectors, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1590-1596.
20. A.H. Shabani, D.A. Clausi, J.S. Zelek, Improved spatio-temporal salient feature detection for action recognition, in: British Machine Vision Conference, 2011.
21. B. Chakraborty, M.B. Holte, T.B. Moeslund, J. Gonzàlez, F.X. Roca, A selective spatio-temporal interest point detector for human action recognition in complex scenes, in: International Conference on Computer Vision, 2011, pp. 1776-1783.
22. P. Matikainen, M. Hebert, R. Sukthankar, Trajectons: action recognition through the motion analysis of tracked features, in: International Conference on Computer Vision Workshops, 2009, pp. 514-521.
23. H. Wang, A. Kläser, C. Schmid, C.-L. Liu, Action recognition by dense trajectories, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 3169-3176.
24. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: ACM Multimedia Conference, 2007, pp. 357-360.
25. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
26. A. Kläser, M. Marszalek, C. Schmid, A spatio-temporal descriptor based on 3d-gradients, in: British Machine Vision Conference, 2008.
27. P. Noriega, B. Bascle, O. Bernier, Local kernel color histograms for background subtraction, in: Computer Vision Theory and Applications, 2006, pp. 213-219.
28. Y. Wang, G. Mori, Learning a discriminative hidden part model for human action recognition, in: Neural Information Processing Systems, 2008, pp. 1721-1728.
29. A. Gilbert, J. Illingworth, and R. Bowden Action recognition using mined hierarchical compound features IEEE Transactions on Pattern Analysis and Machine Intelligence 33 5 2011 883 897
30. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 2046-2053.
31. M. Raptis, I. Kokkinos, S. Soatto, Discovering discriminative action parts from mid-level video representations, in: IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1242-1249.
32. K. Fragkiadaki, J. Shi, Detection free tracking: exploiting motion and topology for segmenting and tracking under entanglement, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 2073-2080.
33. L. Itti, and C. Koch Feature combination strategies for saliency-based visual attention systems Journal of Electronic Imaging 10 1 2001 161 169
34. Z. Ren, S. Gao, D. Rajan, L.-T. Chia, Y. Huang, Spatiotemporal saliency detection via sparse representation, in: IEEE International Conference on Multimedia and Expo, 2012, pp. 158-163.
35. G. Farnebäck, Two-frame motion estimation based on polynomial expansion, in: Scandinavian Conference on Image Analysis, 2003, pp. 363-370.
36. L. Bo, X. Ren, D. Fox, Kernel descriptors for visual recognition, in: Neural Information Processing Systems, 2010.
37. F. Porikli, Integral histogram: a fast way to extract histograms in Cartesian spaces, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 829-836.
38. C. Schüldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: International Conference on Pattern Recognition, 2004, pp. 32-36.
39. R. Messing, C. Pal, H. Kautz, Activity recognition using the velocity histories of tracked keypoints, in: International Conference on Computer Vision, 2009, pp. 104-111.
40. M.D. Rodriguez, J. Ahmed, M. Shah, Action MACH: a spatio-temporal maximum average correlation height filter for action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
41. T. Lan, Y. Wang, G. Mori, Discriminative figure-centric models for joint action localization and recognition, in: IEEE International Conference on Computer Vision, 2011, pp. 2003-2010.
42. M. Raptis, S. Soatto, Tracklet descriptors for action modeling and video analysis, in: European Conference on Computer Vision, 2010, pp. 577-590.
43. N. Ikizler, and P. Duygulu Histogram of oriented rectangles a new pose descriptor for human action recognition Image and Vision Computing 27 10 2009 1515 1526
44. J. Wang, Z. Chen, Y. Wu, Action recognition with multiscale spatio-temporal contexts, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 3185-3192.
45. Y. Benabbas, A. Lablack, N. Ihaddadene, C. Djeraba, Action recognition using direction models of motion, in: International Conference on Pattern Recognition, 2010, pp. 4295-4298.
46. S. Satkin, M. Hebert, Modeling the temporal extent of actions, in: European Conference on Computer Vision, 2010, pp. 536-548.
47. P. Matikainen, M. Hebert, R. Sukthankar, Representing pairwise spatial and temporal relations for action recognition, in: European Conference on Computer Vision, 2010, pp. 508-521.
48. H. Wang, M.M. Ullah, A. Kläser, I. Laptev, C. Schmid, Evaluation of local spatio-temporal features for action recognition, in: British Machine Vision Conference, 2009.