A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition

International Journal of Computer Vision

Volumn 118, Issue 2, 2016, Pages 256-273

  Lin, Liang ^a,b   Wang, Keze ^a,b   Zuo, Wangmeng ^c   Wang, Meng ^d   Luo, Jiebo ^e   Zhang, Lei ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c HARBIN INSTITUTE OF TECHNOLOGY   (China)

^d HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^e University of Rochester   (United States)

Author keywords

Deep learning; Human action and activity; RGB depth analysis; Structured model

Indexed keywords

ITERATIVE METHODS; LEARNING ALGORITHMS; NETWORK LAYERS; NEURAL NETWORKS; PATTERN RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; DEPTH ANALYSIS; GENERALIZATION PERFORMANCE; HUMAN ACTIONS; HUMAN ACTIVITY RECOGNITION; REGULARIZATION TERMS; STATE-OF-THE-ART APPROACH; STRUCTURED MODEL;

DEEP LEARNING;

EID: 84952023029     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-015-0876-z     Document Type: Article

References (57)

1. Amer, M. R., & Todorovic, S. (2012). Sum-product networks for modeling activities with stochastic structure. In CVPR, pp 1314–1321
2. Bayer, J., Osendorfer, C., Korhammer, D., Chen, N., Urban, S., & van der Smagt, P. (2014). On fast dropout and its applicability to recurrent networks. In Proc. ICLR
3. Brendel, W., & Todorovic, S. (2011). Learning spatiotemporal graphs of human activities. In: ICCV, pp 778–785
4. Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1–3), 131–159.
5. Chaquet, J. M., Carmona, E. J., & Fernandez-Caballero, A. (2013). A survey of video datasets for human action and activity recognition. Computer Vision and Image Understanding, 117(6), 633–659.
6. Cheng, Z., Qin, L., Huang, Q., Jiang, S., Yan, S., & Tian, Q. (2011). Human group activity analysis with fusion of motion and appearance information. In ACM Multimedia, pp 1401–1404
7. Chung, K. M., Kao, W. C., Sun, C. L., Wang, L. L., & Lin, C. J. (2003). Radius margin bounds for support vector machines with the rbf kernel. Neural Computation, 15(11), 2643–2681.
8. Do, H., & Kalousis, A. (2013). Convex formulations of radius-margin based support vector machines. In: ICML
9. Do, H., Kalousis, A., & Hilario, M. (2009). Feature weighting using margin and radius based error bound optimization in svms. Machine Learning and Knowledge Discovery in Databases (Vol. 5781, pp. 315–329)., Lecture Notes in Computer Science Berlin Heidelberg: Springer.
10. Donahue, J., Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T. (2015). Long-term recurrent convolutional networks for visual recognition and description. In CVPR
11. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645.
12. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
13. Gupta, R., Chia, A. Y., Rajan, D., Ng E. S., & Lung, E. H. (2013). Human activities recognition using depth images. In ACM Multimedia pp 283–292
14. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
15. Huang, F. J., & LeCun, Y. (2006). Large-scale learning with svm and convolutional for generic object categorization. In CVPR, pp 284–291
16. Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell, 35(1), 221–231.
17. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In CVPR
18. Koppula, H. S., Gupta, R., & Saxena, A. (2013). Learning human activities and object affordances from rgb-d videos. International Journal of Robotics Research (IJRR), 32(8), 951–970.
19. Koppula, H. S., & Saxena, A. (2013). Learning spatio-temporal structure from rgb-d videos for human activity detection and anticipation. In ICML pp 792–800
20. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp 1097–1105
21. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., & Jackel ea, L. D. (1990). Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems
22. Liang, X., Lin, L., & Cao, L. (2013). Learning latent spatio-temporal compositional model for human action recognition. In ACM Multimedia, pp 263–272
23. Lin, L., Wang, X., Yang, W., & Lai, J. H. (2015). Discriminatively trained and-or graph models for object shape detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(5), 959–972.
24. Lin, L., Wu, T., Porway, J., & Xu, Z. (2009). A stochastic graph grammar for compositional object representation and recognition. Pattern Recognition, 42(7), 1297–1307.
25. Luo, P., Tian, Y., Wang, X., & Tang, X. (2014). Switchable deep network for pedestrian detection. In CVPR
26. Luo, P., Wang, X., & Tang, X. (2013a). A deep sum-product architecture for robust facial attributes analysis. In ICCV, pp 2864–2871
27. Luo, P., Wang, X., & Tang, X. (2013b). Pedestrian parsing via deep decompositional neural network. In ICCV, pp 2648–2655
28. Ni, B., Wang, G., & Moulin, P. (2013a). Rgbd-hudaact: A color-depth video database for human daily activity recognition. Consumer Depth Cameras for Computer Vision, Lecture Notes in Computer Science (pp. 193–208). Springer.
29. Ni, B., YPei, Z.L., Lin, L., & Moulin, P. (2013b). Integrating multi-stage depth-induced contextual information for human action recognition and localization. In International Conference and Workshops on Automatic Face and Gesture Recognition, pp 1–8
30. Oreifej, O., & Liu, Z. (2013). Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In CVPR, pp 716–723
31. Packer, B., Saenko, K., & Koller, D. (2012). A combined pose, object, and feature model for action understanding. In CVPR, pp 1378–1385
32. Pei, M., Jia, Y., & Zhu, S. (2011). Parsing video events with goal inference and intent prediction. In ICCV, pp 487–494
33. Sadanand, S., & Corso, J. J. (2012). Action bank: A high-level representation of activity in video. In CVPR, pp 1234–1241
34. Scovanner, P., Ali, S., & Shah, M. (2007) A 3-dimensional sift descriptor and its application to action recognition. In ACM Multimedia, pp 357–360
35. Sermanet, P., Kavukcuoglu, K., Chintala, S., & LeCun, Y. (2013). Pedestrian detection with unsupervised multi- stage feature learning. In CVPR
36. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overtting. The Journal of Machine Learning Research, 15(1), 1929–1958.
37. Sung, J., Ponce, C., Selman, B., & Saxena, A. (2012) Unstructured human activity detection from rgbd images. In ICRA, pp 842–849
38. Tang, K., Fei-Fei, L., & Koller, D. (2012). Learning latent temporal structure for complex event detection. In CVPR, pp 1250–1257
39. Tu, K., Meng, M., Lee, M. W., Choi, T., & Zhu, S. (2014). Joint video and text parsing for understanding events and answering queries. IEEE Transactions on Multimedia, 21(2), 42–70.
40. Vapnik, V. (1998). Statistical learning theory. New York: John Wiley and Sons.
41. Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., & Saenko, K. (2015). Translating videos to natural language using deep recurrent neural networks. In North American Chapter of the Association for Computational Linguistics
42. Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2012). Mining actionlet ensemble for action recognition with depth cameras. In CVPR, pp 1290–1297
43. Wang, Y., & Mori, G. (2011). Hidden part models for human action recognition: Probabilistic vs. max-margin. IEEE Trans Pattern Anal Mach Intell, 33(7), 1310–1323.
44. Wang, K., Wang, X., Lin, L. (2014). 3d human activity recognition with reconfigurable convolutional neural networks. In ACM MM
45. Wang, C., Wang, Y., & Yuille, A. L. (2013). An approach to pose-based action recognition. In CVPR, pp 915–922
46. Wang, J., & Wu, Y. (2013) Learning maximum margin temporal warping for action recognition. In ICCV, pp 2688–2695
47. Wu, C. F. J. (1983). On the convergence properties of the em algorithm. Annals of Statistics, 11(1), 95–103.
48. Wu, P., Hoi, S., Xia, H., Zhao, P., Wang, D., & Miao, C. (2013) Online multimodal deep similarity learning with application to image retrieval. In ACM Mutilmedia, pp 153–162
49. Xia, L., & Aggarwal, J. (2013) Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In CVPR, pp 2834–2841
50. Xia, L., Chen, C., & Aggarwal, J. (2012a). View invariant human action recognition using histograms of 3d joints. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, pp 20–27
51. Xia, L., Chen, C., & Aggarwal, J. K. (2012b). View invariant human action recognition using histograms of 3d joints. In CVPRW, pp 20–27
52. Yang, X., Zhang, C., & Tian, Y. (2012). Recognizing actions using depth motion maps-based histograms of oriented gradients. In ACM Multimedia, pp 1057–1060
53. Yu, K., Lin, Y., & Lafferty, J. (2011). Learning image representations from the pixel level via hierarchical sparse coding. In CVPR, pp 1713–1720
54. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., & Samaras, D. (2012) Two-person interaction detection using body-pose features and multiple instance learning. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE
55. Zhao, X., Liu, Y., & Fu, Y. (2013). Exploring discriminative pose sub-patterns for effective action classification. In: ACM Multimedia, pp 273–282
56. Zhou, X., Zhuang, X., Yan, S., Chang, S. F., Johnson, M. H., & Huang, T. S. (2009) Sift-bag kernel for video event analysis. In ACM Multimedia, pp 229–238
57. Zhu, S., & Mumford, D. (2007). A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision, 2(4), 259–362.