Multimodal dynamic networks for gesture recognition

MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Volumn , Issue , 2014, Pages 945-948

  Wu, Di ^a   Shao, Ling ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Deep Belief Networks; Gesture recognition; Human computer interaction; Multimodal fusion

Indexed keywords

DEEP LEARNING; HUMAN COMPUTER INTERACTION;

DEEP BELIEF NETWORKS; EMISSION PROBABILITIES; MULTI-MODAL FUSION; MULTIMODAL DYNAMICS; MULTIMODAL FEATURES; REAL WORLD SITUATIONS; SHARED REPRESENTATIONS; SIGN LANGUAGE RECOGNITION;

GESTURE RECOGNITION;

EID: 84913600686     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2647868.2654969     Document Type: Conference Paper

References (13)

1. R. Chaudhry, F. Ofli, G. Kurillo, R. Bajcsy, and R. Vidal. Bio-inspired dynamic 3d discriminative skeletal features for human action recognition. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013.
2. S. Escalera, J. Gonzàlez, X. Baró, M. Reyes, O. Lopés, I. Guyon, V. Athitsos, and H. J. Escalante. Multi-modal gesture recognition challenge 2013: Dataset and results. In ChaLearn Multi-Modal Gesture Recognition Grand Challenge and Workshop. ACM, 2013.
3. S. Fothergill, H. M. Mentis, P. Kohli, and S. Nowozin. Instructing people for training gestural interactive systems. In CHI. ACM, 2012.
4. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
5. A. Mohamed, G. E. Dahl, and G. Hinton. Acoustic modeling using deep belief networks. Audio, Speech, and Language Processing, IEEE Transactions on, 2012.
6. M. Müller and T. Röder. Motion templates for automatic classification and retrieval of motion capture data. In SIGGRAPH/Eurographics symposium on Computer animation. Eurographics Association, 2006.
7. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. Multimodal deep learning. In International Conference on Machine Learning. IEEE, 2011.
8. S. Nowozin and J. Shotton. Action points: A representation for low-latency online human action recognition. Technical report, Technical report, 2012.
9. F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy. Sequence of the most informative joints (smij): A new representation for human skeletal action recognition. Journal of Visual Communication and Image Representation, 2013.
10. N. Srivastava and R. Salakhutdinov. Multimodal learning with deep boltzmann machines. In Neural Information Processing Systems, 2012.
11. A. Torralba and A. A. Efros. Unbiased look at dataset bias. In IEEE Conference on Computer Vision and Pattern Recognition, 2011.
12. D. Wu and L. Shao. Leveraging hierarchical parametric networks for skeletal joints based action segmentation and recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2014.
13. X. Yang and Y. Tian. Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2012.