MoFAP: A Multi-level Representation for Action Recognition

International Journal of Computer Vision

Volumn 119, Issue 3, 2016, Pages 254-271

  Wang, Limin ^a   Qiao, Yu ^b   Tang, Xiaoou ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

Action recognition; Motion Atom; Motion Feature; Motion Phrase

Indexed keywords

CHEMICAL ACTIVATION; MOTION ESTIMATION; VIDEO RECORDING;

ACTION RECOGNITION; DISCRIMINATIVE CLUSTERING; MOTION FEATURES; MOTION INFORMATION; MOTION PHRASE; STATE-OF-THE-ART PERFORMANCE; TEMPORAL STRUCTURES; VIDEO REPRESENTATIONS;

ATOMS;

EID: 84944683002     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-015-0859-0     Document Type: Article

References (60)

1. Aggarwal, J. K., & Ryoo, M. S. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3), 16.
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In VLDB (pp. 487–499).
3. Amer, M. R., Xie, D., Zhao, M., Todorovic, S., & Zhu, S. C. (2012). Cost-sensitive top-down/bottom-up inference for multiscale activity recognition. In ECCV (pp. 187–200).
4. Berg, T. L., Berg, A. C., & Shih, J. (2010). Automatic attribute discovery and characterization from noisy web data. In ECCV (pp. 663–676).
5. Bishop, C. (2006). Pattern recognition and machine learning (Vol. 4). Berlin: Springer.
6. Bourdev, L. D., & Malik, J. (2009). Poselets: Body part detectors trained using 3d human pose annotations. In ICCV (pp. 1365–1372).
7. Cai, Z., Wang, L., Peng, X., & Qiao, Y. (2014). Multi-view super vector for action recognition. In CVPR (pp. 596–603).
8. Chang, C. C., & Lin, C. J. (2011). Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.
9. Chen, Y., Zhu, L., Lin, C., Yuille, A. L., & Zhang, H. (2007). Rapid inference on a novel and/or graph for object detection, segmentation and parsing. In NIPS.
10. Csurka, G., Dance, C., Fan, L., Willamowski, J., & Bray, C. (2004). Visual categorization with bags of keypoints. In Workshop on statistical learning in computer vision, ECCV, Prague (Vol. 1, pp. 1–2).
11. Doersch, C., Gupta, A., & Efros, A. A. (2013). Mid-level visual element discovery as discriminative mode seeking. In NIPS pp. 494–502.
12. Felzenszwalb, P. F., Girshick, R. B., McAllester, D. A., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions Pattern Analysis and Machine Intelligence, 32(9), 1627–1645.
13. Forsyth, D. A., Arikan, O., Ikemoto, L., O’Brien, J. F., & Ramanan, D. (2005). Computational studies of human motion: Part 1, tracking and motion synthesis. Foundations and Trends in Computer Graphics and Vision 1(2/3).
14. Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315, 972–976.
15. Gaidon, A., Harchaoui, Z., & Schmid, C. (2013). Temporal localization of actions with actoms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2782–2795.
16. Gaidon, A., Harchaoui, Z., & Schmid, C. (2014). Activity representation with motion hierarchies. International Journal of Computer Vision, 107(3), 219–238.
17. Gorelick, L., Blank, M., Shechtman, E., Irani, M., & Basri, R. (2007). Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2247–2253.
18. Jain, A., Gupta, A., Rodriguez, M., & Davis, L. S. (2013a). Representing videos using mid-level discriminative patches. In CVPR (pp. 2571–2578).
19. Jain, M., Jegou, H., & Bouthemy, P. (2013b). Better exploiting motion for better action recognition. In CVPR (pp. 2555–2562).
20. Jiang, Y., Dai, Q., Xue, X., Liu, W., & Ngo, C. (2012). Trajectory-based modeling of human actions with motion reference points. In ECCV (pp. 425–438).
21. Jiang, Y. G., Liu, J., Roshan Zamir, A., Laptev, I., Piccardi, M., Shah, M., & Sukthankar, R. (2013). THUMOS challenge: Action recognition with a large number of classes. http://crcv.ucf.edu/ICCV13-Action-Workshop/.
22. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In CVPR (pp. 1725–1732).
23. Kliper-Gross, O., Gurovich, Y., Hassner, T., & Wolf, L. (2012). Motion interchange patterns for action recognition in unconstrained videos. In ECCV (pp. 256–269).
24. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011). HMDB: A large video database for human motion recognition. In ICCV (pp. 2556–2563).
25. Laptev, I. (2005). On space-time interest points. International Journal of Computer Vision, 64(2–3), 107–123.
26. Laxton, B., Lim, J., & Kriegman, D. J. (2007). Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video. In CVPR (pp. 1–8).
27. Liu, J., Kuipers, B., & Savarese, S. (2011). Recognizing human actions by attributes. In CVPR (pp. 3337–3344).
28. Niebles, J. C., Chen, C. W., & Li, F. F. (2010). Modeling temporal structure of decomposable motion segments for activity classification. In ECCV (pp. 392–405).
29. Oliver, N., Rosario, B., & Pentland, A. (2000). A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 831–843.
30. Parikh, D., & Grauman, K. (2011). Relative attributes. In ICCV (pp. 503–510).
31. Pirsiavash, H., & Ramanan, D. (2014). Parsing videos of actions with segmental grammars. In CVPR (pp. 612–619).
32. Raptis, M., Kokkinos, I., & Soatto, S. (2012). Discovering discriminative action parts from mid-level video representations. In CVPR (pp. 1242–1249).
33. Reddy, K. K., & Shah, M. (2013). Recognizing 50 human action categories of web videos. Machine Vision and Applications, 24(5), 971–981.
34. Rohrbach, M., Regneri, M., Andriluka, M., Amin, S., Pinkal, M., & Schiele, B. (2012). Script data for attribute-based recognition of composite activities. In ECCV.
35. Sadanand, S., & Corso, J. J. (2012). Action bank: A high-level representation of activity in video. In CVPR (pp. 1234–1241).
36. Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. J. (2013). Image classification with the fisher vector: Theory and practice. International Journal of Computer Vision, 105(3), 222–245.
37. Sapienza, M., Cuzzolin, F., & Torr, P. H. S. (2012). Learning discriminative space-time actions from weakly labelled videos. In BMVC (pp. 1–12).
38. Schüldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local svm approach. In ICPR.
39. Si, Z., & Zhu, S. C. (2013). Learning AND-OR templates for object recognition and detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2189–2205.
40. Simonyan, K., & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. In NIPS (pp. 568–576).
41. Singh, S., Gupta, A., & Efros, A. A. (2012). Unsupervised discovery of mid-level discriminative patches. In ECCV (pp. 73–86).
42. Sivic, J., & Zisserman, A. (2003). Video google: A text retrieval approach to object matching in videos. In ICCV (pp. 1470–1477).
43. Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. CoRR abs/1212.0402.
44. Tang, K. D., Li, F. F., & Koller, D. (2012). Learning latent temporal structure for complex event detection. In CVPR (pp. 1250–1257).
45. Turaga, P. K., Chellappa, R., Subrahmanian, V. S., & Udrea, O. (2008). Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 18(11), 1473–1488.
46. Wang, H., & Schmid, C. (2013a). Action recognition with improved trajectories. In ICCV (pp. 3551–3558).
47. Wang, H., & Schmid, C. (2013b). Lear-inria submission for the thumos workshop. In: ICCV Workshop on Action Recognition with a Large Number of Classes.
48. Wang, H., Kläser, A., Schmid, C., & Liu, C. L. (2013a). Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1), 60–79.
49. Wang, L., Qiao, Y., & Tang, X. (2013b). Mining motion atoms and phrases for complex action recognition. In ICCV (pp. 2680–2687).
50. Wang, L., Qiao, Y., & Tang, X. (2013c). Motionlets: Mid-level 3D parts for human motion recognition. In CVPR (pp. 2674–2681).
51. Wang, L., Qiao, Y., & Tang, X. (2014a). Latent hierarchical model of temporal structure for complex activity classification. IEEE Transactions on Image Processing, 23(2), 810–822.
52. Wang, L., Qiao, Y., & Tang, X. (2014b). Video action detection with relational dynamic-poselets. In ECCV (pp. 565–580).
53. Wang, L., Qiao, Y., & Tang, X. (2015). Action recognition with trjectory-pooled deep-convolutional descriptors. In CVPR (pp. 4305–4314).
54. Wang, S. B., Quattoni, A., Morency, L. P., Demirdjian, D., & Darrell, T. (2006). Hidden conditional random fields for gesture recognition. In CVPR (pp. 1521–1527).
55. Wang, X., Wang, L., & Qiao, Y. (2012). A comparative study of encoding, pooling and normalization methods for action recognition. In ACCV (pp. 572–585).
56. Wu, J., Zhang, Y., & Lin, W. (2014). Towards good practices for action video encoding. In CVPR (pp. 2577–2584).
57. Yao, B., & Li, F. F. (2010). Grouplet: A structured image representation for recognizing human and object interactions. In CVPR.
58. Zhang, W., Zhu, M., & Derpanis, K. G. (2013). From actemes to action: A strongly-supervised representation for detailed action understanding. In ICCV (pp. 2248–2255).
59. Zhao, Y., & Zhu S. C. (2011). Image parsing with stochastic scene grammar. In NIPS (pp. 73–81).
60. Zhu,J., Wang, B., Yang, X., Zhang, W., & Tu, Z. (2013). Action recognition with actons. In ICCV (pp. 3559–3566).