A survey on still image based human action recognition

Pattern Recognition

Volumn 47, Issue 10, 2014, Pages 3343-3361

  Guo, Guodong ^a   Lai, Alice ^a  

^a West Virginia University   (United States)

Author keywords

Action recognition; Databases; Evaluation; Still image based; Survey; Various cues

Indexed keywords

DATABASE SYSTEMS; GESTURE RECOGNITION; IMAGE RETRIEVAL; INTERNET; PATTERN RECOGNITION; RESEARCH; SURVEYING; SURVEYS;

ACTION RECOGNITION; CLUTTERED BACKGROUNDS; EVALUATION; HUMAN-ACTION RECOGNITION; SPATIO TEMPORAL FEATURES; STATE-OF-THE-ART METHODS; STILL IMAGES; VARIOUS CUES;

MOTION ESTIMATION;

EID: 84902318725     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.04.018     Document Type: Article

References (81)

1. C. Cedras, and M. Shah Motion-based recognition a survey Image Vis. Comput. 13 1995 129 155
2. J. Aggarwal, and Q. Cai Human motion analysis a review Comput. Vis. Image Underst. 73 1999 428 440
3. D. Gavrila The visual analysis of human movement a survey Comput. Vis. Image Underst. 73 1999 82 98
4. V. Kruger, D. Kragic, A. Ude, and C. Geib The meaning of action a review on action recognition and mapping Adv. Robot. 21 2007 1473 1501
5. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea Machine recognition of human activities a survey IEEE Trans. Circuits Syst. Video Technol. 18 2008 1473 1488
6. R. Poppe A survey on vision-based human action recognition Image Vis. Comput. 28 2010 976 990
7. X. Ji, and H. Liu Advances in view-invariant human motion analysis a review IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40 2010 13 24
8. J. Aggarwal, and M. Ryoo Human activity analysis a review ACM Comput. Surv. 43 2011 16
9. M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman The Pascal visual object classes (VOC) challenge Int. J. Comput. Vis. 88 2010 303 338
10. B. Fasel, and J. Luettin Automatic facial expression analysis a survey Pattern Recognit. 36 2003 259 275
11. Z. Zeng, M. Pantic, G.I. Roisman, and T.S. Huang A survey of affect recognition methods audio, visual, and spontaneous expressions IEEE Trans. Pattern Anal. Mach. Intell. 31 2009 39 58
12. C. Desai, D. Ramanan, Detecting actions, poses, and objects with relational phraselets, in: European Conference on Computer Vision Workshops and Demonstrations, Springer-Verlag, 2012, pp. 158-172.
13. A. Prest, C. Schmid, and V. Ferrari Weakly supervised learning of interactions between humans and objects IEEE Trans. Pattern Anal. Mach. Intell. 34 2012 601 614
14. F.S. Khan, M.A. Rao, J. van de Weijer, A.D. Bagdanov, A. Lopez, and M. Felsberg Coloring action recognititon in still images Int. J. Comput. Vis. 2013 1 17
15. I. Laptev On space-time interest points Int. J. Comput. Vis. 64 2005 107 123
16. Y. Wang, H. Jiang, M. Drew, Z.-N. Li, G. Mori, Unsupervised discovery of action classes, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2006, pp. 1654-1661.
17. L.-J. Li, L. Fei-Fei, What, where and who? classifying events by scene and object recognition, in: IEEE Conference on Computer Vision, IEEE, 2007, pp. 1-8.
18. C. Thurau, V. Hlavac, Pose primitive based human action recognition in videos or still images, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1-8.
19. N. Ikizler, R. Cinbis, S. Pehlivan, P. Duygulu, Recognizing actions from still images, in: International Conference on Pattern Recognition, IEEE, 2008, pp. 1-4.
20. N. Ikizler-Cinbis, R. G. Cinbis, S. Sclaroff, Learning actions from the Web, in: IEEE International Conference on Computer Vision, IEEE, 2009, pp. 995-1002.
21. A. Gupta, A. Kembhavi, and L. Davis Observing human-object interactions using spatial and functional compatibility for recognition IEEE Trans. Pattern Anal. Mach. Intell. 31 2009 1775 1789
22. B. Yao, L. Fei-Fei, Grouplet: A structured image representation for recognizing human and object interactions, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, San Francisco, USA, 2010, pp. 9-16.
23. C. Desai, D. Ramanan, C. Fowlkes, Discriminative models for static human-object interactions, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2010, pp. 9-16.
24. W. Yang, Y. Wang, G. Mori, Recognizing human actions from still images with latent poses, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 2030-2037.
25. B. Yao, L. Fei-Fei, Modeling mutual context of object and human pose in human-object interaction activities, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 17-24.
26. V. Delaitre, I. Laptev, J. Sivic, Recognizing human actions in still images: a study of bag-of-features and part-based representations, in: Proceedings of the British Machine Vision Conference, BMVA, 2010, p. 7.
27. N. Shapovalova, W. Gong, M. Pedersoli, F. X. Roca, J. Gonzalez, On importance of interactions and context in human action recognition, in: Pattern Recognition and Image Analysis: 5th Iberian Conference, vol. 6669, Springer, 2011, p. 58.
28. S. Maji, L. Bourdev, J. Malik, Action recognition from a distributed representation of pose and appearance, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2011, pp. 3177-3184.
29. B. Yao, A. Khosla, L. Fei-Fei, Combining randomization and discrimination for fine-grained image categorization, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2011, pp. 1577-1584.
30. B. Yao, A. Khosla, L. Fei-Fei, Classifying actions and measuring action similarity by modeling the mutual context of objects and human poses, in: International Conference on Machine Learning, Omnipress, Bellevue, USA, 2011, p. D3.
31. K. Raja, I. Laptev, P. Pérez, L. Oisel, Joint pose estimation and action recognition in image graphs, in: IEEE International Conference on Image Processing, IEEE, 2011, pp. 25-28.
32. P. Koniusz, K. Mikolajczyk, Soft assignment of visual words as linear coordinate coding and optimisation of its reconstruction error, in: IEEE International Conference on Image Processing, IEEE, 2011, pp. 2413-2416.
33. P. Koniusz, K. Mikolajczyk, Spatial coordinate coding to reduce histogram representations, dominant angle and colour pyramid match, in: IEEE International Conference on Image Processing, IEEE, 2011, pp. 661-664.
34. P. Li, J. Ma, S. Gao, Actions in still web images: visualization, detection and retrieval, in: Web-Age Information Management: 12th International Conference, vol. 6897, Springer, 2011, p. 302.
35. B. Yao, X. Jiang, A. Khosla, A. Lin, L. Guibas, L. Fei-Fei, Human action recognition by learning bases of action attributes and parts, in: IEEE International Conference on Computer Vision, IEEE, 2011, pp. 1331-1338.
36. V. Delaitre, J. Sivic, I. Laptev, Learning person-object interactions for action recognition in still images, in: Advances in Neural Information Processing Systems (NIPS), MIT Press, 2011.
37. P. Li, J. Ma, What is happening in a still picture?, in: IEEE Asian Conference on Pattern Recognition, IEEE, 2011, pp. 32-36.
38. B. Yao, and L. Fei-Fei Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses IEEE Trans. Pattern Anal. Mach. Intell. 99 2012 1691 1703
39. G. Sharma, F. Jurie, C. Schmid, Discriminative spatial saliency for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012, pp. 3506-3513.
40. B. Yao, L. Fei-Fei, Action recognition with exemplar based 2.5 d graph matching, in: European Conference on Computer Vision Workshops and Demonstrations, Springer-Verlag, 2012, pp. 173-186.
41. M.P. Kumar, B. Packer, D. Koller, Modeling latent variable uncertainty for loss-based learning, in: Proceedings of International Conference on Machine Learning, Omnipress, New York, NY, USA, 2012, pp. 465-472.
42. F. Sener, C. Bas, N. Ikizler-Cinbis, On recognizing actions in still images via multiple features, in: European Conference on Computer Vision Workshops and Demonstrations, Springer, 2012, pp. 263-272.
43. Y. Zheng, Y.-J. Zhang, X. Li, B.-D. Liu, Action recognition in still images using a combination of human pose and context information, in: IEEE International Conference on Image Processing, IEEE, 2012, pp. 785-788.
44. N. Ikizler-Cinbis, and S. Sclaroff Web-based classifiers for human action recognition IEEE Trans. Multimed. 14 2012 1031 1045
45. D.T. Le, R. Bernardi, J. Uijlings, Exploiting language models to recognize unseen actions, in: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, ACM, 2013, pp. 231-238.
46. G. Sharma, F. Jurie, C. Schmid, Expanded Parts Model for Human Attribute and Action Recognition in Still Images, in: IEEE Conference on Computer Vision Pattern Recognition, IEEE, Portland, OR, United States, 2013.
47. J. Canny A computational approach to edge detection IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 1986 679 698
48. D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization, in: Advances in Neural Information Processing Systems (NIPS), MIT Press, 2000, pp. 556-562.
49. D. Ramanan, Learning to parse images of articulated bodies, in: Advances in Neural Information Processing Systems (NIPS), MIT press, 2006, pp. 1129-1136.
50. L. Breiman Random forests Mach. Learn. 45 2001 5 32
51. A. Farhadi, I. Endres, D. Hoiem, D. Forsyth, Describing objects by their attributes, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 1778-1785.
52. L. Bourdev, J. Malik, Poselets: Body part detectors trained using 3d human pose annotations, in: IEEE International Conference on Computer Vision, IEEE, 2009, pp. 1365-1372.
53. B. Sapp, A. Toshev, B. Taskar, Cascaded models for articulated pose estimation, in: European Conference on Computer Vision, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 406-420.
54. C.J. Taylor Reconstruction of articulated objects from point correspondences in a single uncalibrated image Comput. Vis. Image Underst. 80 2000 349 363
55. H.-J. Lee, and Z. Chen Determination of 3d human body postures from a single view Comput. Vis. Graph. Image Process. 30 1985 148 168
56. B. Alexe, T. Deselaers, V. Ferrari, What is an object?, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2010, pp. 73-80.
57. Y. Chen, J. Bi, and J. Wang Miles multiple-instance learning via embedded instance selection IEEE Trans. Pattern Anal. Mach. Intell. 28 2006 1931 1947
58. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: A large-scale hierarchical image database, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 248-255.
59. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan Object detection with discriminatively trained part-based models IEEE Trans. Pattern Anal. Mach. Intell. 32 2010 1627 1645
60. C. Desai, D. Ramanan, C. Fowlkes, Discriminative models for multi-class object layout, in: IEEE International Conference on Computer Vision, IEEE, 2009, pp. 229-236.
61. A. Oliva, and A. Torralba Modeling the shape of the scene a holistic representation of the spatial envelope Int. J. Comput. Vis. 42 2001 145 175
62. D. Lowe, Object recognition from local scale-invariant features, in: IEEE International Conference on Computer Vision, vol. 2, IEEE, 1999, pp. 1150-1157.
63. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2005, pp. 886-893.
64. S. Belongie, J. Malik, Matching with shape contexts, in: IEEE Workshop on Content-based Access of Image and Video Libraries, IEEE, 2000, pp. 20-26.
65. A. Friedman Framing pictures the role of knowledge in automatized encoding and memory for gist J. Exp. Psychol.: General 108 1979 316 355
66. H. Bay, T. Tuytelaars, L. V. Gool, Surf: Speeded up robust features, in: European Conference on Computer Vision, Springer, Berlin, Heidelberg, 2006, pp. 404-417.
67. N. Ikizler, P. Duygulu, Human action recognition using distribution of oriented rectangular patches, in: Workshop on Human Motion, Springer, 2007, pp. 271-284.
68. M. Tahir, J. Kittler, K. Mikolajczyk, F. Yan, K. van de Sande, T. Gevers, Visual category recognition using spectral regression and kernel discriminant analysis, in: IEEE International Conference on Computer Vision Workshops, IEEE, 2009, pp. 178-185.
69. B. Schölkopf, and A.J. Smola Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond 2001 MIT press
70. Y. Chen, L. Zhu, C. Lin, A. L. Yuille, H. Zhang, Rapid inference on a novel and/or graph for object detection, segmentation and parsing, in: Advances in Neural Information Processing Systems (NIPS), MIT Press, 2007, pp. 289-296.
71. Y. Yang, D. Ramanan, Articulated pose estimation with flexible mixtures-of-parts, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2011, pp. 1385-1392.
72. M. Kumar, A. Zisserman, P. Torr, Efficient discriminative learning of parts-based models, in: IEEE International Conference on Computer Vision, IEEE, 2009, pp. 552-559.
73. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, 2006, pp. 2169-2178.
74. J. Shi, and J. Malik Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Mach. Intell. 22 2000 888 905
75. J.H. Ward Hierarchical grouping to optimize an objective function J. Am. Stat. Assoc. 58 1963 236 244
76. R. Hamid, A. Johnson, S. Batta, A. Bobick, C. Isbell, G. Coleman, Detection and explanation of anomalous activities: representing activities as bags of event n-grams, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2005, pp. 1031-1038.
77. S.T. Hedetniemi, and R.C. Laskar Bibliography on domination in graphs and some basic definitions of domination parameters Discret. Math. 86 1991 257 277
78. B. Yao, H. Ai, S. Lao, Building a compact relevant sample coverage for relevance feedback in content-based image retrieval, in: European Conference on Computer Vision, Springer-Verlag, Berlin, Heidelberg, 2008, pp. 697-710.
79. M. Grubinger, P. Clough, H. Müller, T. Deselaers, The IAPR TC-12 benchmark: a new evaluation resource for visual information systems, in: International Workshop OntoImage, ESSLLI, 2006, pp. 13-23.
80. M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, in: IEEE International Conference on Computer Vision, IEEE, 2005, pp. 1395-1402.
81. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: IEEE International Conference on Pattern Recognition, vol. 3, IEEE, 2004, pp. 32-36.