Exploiting the deep learning paradigm for recognizing human actions

11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014

Volumn , Issue , 2014, Pages 93-98

  Foggia, Pasquale ^a   Saggese, Alessia ^a   Strisciuglio, Nicola ^a   Vento, Mario ^a  

^a UNIVERSITY OF SALERNO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; LARGE DATASET; SECURITY SYSTEMS;

DEEP BELIEF NETWORKS; DESCRIPTION SCHEMES; FEATURE VECTORS; HAND-DESIGN; HUMAN ACTIONS; LEARNING PARADIGMS; RESTRICTED BOLTZMANN MACHINE; STATE-OF-THE-ART ALGORITHMS;

DEEP LEARNING;

EID: 84909953054     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AVSS.2014.6918650     Document Type: Conference Paper

References (25)

1. G. Acampora, P. Foggia, A. Saggese, and M. Vento. Combining neural networks and fuzzy systems for human behavior understanding. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on, pages 88-93, Sept 2012.
2. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt. Sequential deep learning for human action recognition. In A. Salah and B. Lepri, editors, Human Behavior Understanding, volume 7065 of Lecture Notes in Computer Science, pages 29-39. Springer Berlin Heidelberg, 2011.
3. Y. Bengio. Learning deep architectures for ai. Found. Trends Mach. Learn., 2(1):1-127, Jan. 2009.
4. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE T Pattern Anal., 35(8):1798-1828, Aug 2013.
5. L. Brun, G. Percannella, A. Saggese, and M. Vento. Recognition of human actions by kernels of visual strings. In Advanced Video and Signal-Based Surveillance (AVSS), 2014 IEEE International Conference on, 2014.
6. L. Brun, A. Saggese, and M. Vento. Dynamic scene understanding for behavior analysis based on string kernels. Circuits and Systems for Video Technology, IEEE Transactions on, 2014.
7. V. Carletti, P. Foggia, G. Percannella, A. Saggese, and M. Vento. Recognition of human actions from rgb-d videos using a reject option. In A. Petrosino, L. Maddalena, and P. Pala, editors, New Trends in Image Analysis and Processing ICIAP 2013, volume 8158 of Lecture Notes in Computer Science, pages 436-445. Springer Berlin Heidelberg, 2013.
8. Y. Chen, Q. Wu, and X. He. Human action recognition based on radon transform. In W. Lin, D. Tao, J. Kacprzyk, Z. Li, E. Izquierdo, and H. Wang, editors, Multimedia Analysis, Processing and Communications, volume 346 of Studies in Computational Intelligence, pages 369-389. Springer Berlin Heidelberg, 2011.
9. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on, pages 65-72, Oct 2005.
10. P. Foggia, G. Percannella, A. Saggese, and M. Vento. Recognizing human actions by a bag of visual words. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pages 2910-2915, Oct 2013.
11. G. E. Hinton. Deep belief networks. Scholarpedia, 4(5):5947, 2009.
12. G. E. Hinton. A practical guide to training restricted boltzmann machines. In Neural Networks: Tricks of the Trade (2nd ed.), pages 599-619. 2012.
13. J. Jang, Y. Park, and I. Suh. Empirical evaluation on deep learning of depth feature for human activity recognition. In M. Lee, A. Hirose, Z.-G. Hou, and R. Kil, editors, Neural Information Processing, volume 8228 of Lecture Notes in Computer Science, pages 576-583. Springer Berlin Heidelberg, 2013.
14. S. Ji, W. Xu, M. Yang, and K. Yu. 3d convolutional neural networks for human action recognition. IEEE T Pattern Anal., 35(1):221-231, Jan 2013.
15. Q. Le, W. Zou, S. Yeung, and A. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, pages 3361-3368, June 2011.
16. N. Le Roux and Y. Bengio. Representational power of restricted boltzmann machines and deep belief networks. Neural Comput., 20(6):1631-1649, June 2008.
17. W. Li, Q. Yu, H. Sawhney, and N. Vasconcelos. Recognizing activities via bag of words for attribute dynamics. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 2587-2594, June 2013.
18. V. Megavannan, B. Agarwal, and R. Babu. Human action recognition using depth maps. In Signal Processing and Communications (SPCOM), 2012 International Conference on, pages 1-5, july 2012.
19. G. Montufar and N. Ay. Refinements of universal approximation results for deep belief networks and restricted boltzmann machines. Neural Comput., 23(5):1306-1319, May 2011.
20. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy. Berkeley mhad: A comprehensive multimodal human action database. In IEEE Workshop on Applications on Computer Vision (WACV). IEEE, 2013.
21. R. Poppe. A survey on vision-based human action recognition. Image Vision Comput., 28(6):976-990, 2010.
22. A. Romero and C. Gatta. Do we really need all these neurons? In Pattern Recognition and Image Analysis, volume 7887 of Lecture Notes in Computer Science, pages 460-467. Springer Berlin Heidelberg, 2013.
23. R. Salakhutdinov and G. E. Hinton. Deep boltzmann machines. In AISTATS, pages 448-455, 2009.
24. S. Vishwakarma and A. Agrawal. A survey on activity recognition and behavior understanding in video surveillance. Visual Comput., 29(10):983-1009, 2013.
25. C. Yuan, X. Li, W. Hu, H. Ling, and S. Maybank. 3d r transform on spatio-temporal interest points for action recognition. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 724-730, June 2013.