A multi-scale approach to gesture detection and recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 484-491

  Neverova, Natalia ^a   Wolf, Christian ^a   Paci, Giulio ^b   Sommavilla, Giacomo ^b   Taylor, Graham W ^c   Nebout, Florian ^d  

^a UNIVERSITÉ DE LYON   (France)

^b CNR   (Italy)

^c UNIVERSITY OF GUELPH   (Canada)

^d Awabot   (France)

Author keywords

Action recognition; Convolutional neural networks; Gesture recognition; Multimodal systems; Recurrent neural networks

Indexed keywords

DATA FUSION; RECURRENT NEURAL NETWORKS;

ACTION RECOGNITION; CONVOLUTIONAL NEURAL NETWORK; GESTURE CLASSIFICATIONS; HUMAN GESTURE RECOGNITION; INDIVIDUAL CLASSIFIERS; MULTI-SCALE APPROACHES; MULTIMODAL SYSTEM; SPATIAL AND TEMPORAL SCALE;

GESTURE RECOGNITION;

EID: 84897562004     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2013.69     Document Type: Conference Paper

References (40)

1. M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt. Spatio-Temporal Convolutional Sparse Auto- Encoder for Sequence Classification. In BMVC, pages 124.1-124.12, 2012.
2. O. Çeliktutan, C. B. Akgül, C. Wolf, and B. Sankur. Graph- Based Analysis of Physical Exercise Actions. In 1st ACM MM Workshop on Multimedia Indexing and Information Retrieval for Healthcare (MIIRH), 2013.
3. B. Chen, J.-A. Ting, B. Marlin, and N. de Freitas. Deep learning of invariant Spatio-Temporal Features from Video. In NIPSWorkshop on Deep Learning and Unsupervised Feature Learning, 2010.
4. F. Cutugno, A. Origilia, and D. Seppi. Evalita 2011: Forced alignment task. In Evaluation of Natural Language and Speech Tools for Italian, pages 305-311, 2013.
5. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie. Behavior Recognition via Sparse Spatio-Temporal Features. In 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pages 65-72, 2005.
6. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. PAMI, 32(9):1627-45, Oct. 2010.
7. G.Taylor, R.Fergus, Y.LeCun, and C.Bregler. Convolutional learning of spatio-temporal features. In ECCV, 2010.
8. I. Guyon, V. Athitsos, P. Jangyodsuk, and B. Hamner. ChaLearn Gesture Challenge: Design and First Results. In CVPRWorkshop on Gesture Recognition and Kinect Demonstration Competition, pages 1-6, 2012.
9. S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. In A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, 2001.
10. S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, 1997.
11. I.Laptev, M.Marszalek, C.Schmid, and B.Rozenfeld. Learning realistic human actions from movies. CVPR, 2008
12. H. Jaeger and H. Haas. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science, 304(78):78-80, Apr. 2004.
13. S. Ji, W. Xu, M. Yang, and K. Yu. 3D Convolutional Neural Networks for Human Action Recognition. PAMI, 35(1):221-31, Jan. 2013.
14. R. Khan and N. Ibraheem. Hand gesture recognition: a literature review. International Journal of Artificial Intelligence & Applications (IJAIA), 3(4):161-174, 2012.
15. A. Kläser, M. Marszalek, and C. Schmid. A spatio-temporal descriptor based on 3D-gradients. In BMVC, 2008.
16. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In CVPR, pages 3361-3368. Ieee, June 2011.
17. Y. LeCun and L. Bottou. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
18. A. Lee, T. Kawahara, and K. Shikano. Julius - an open source real-time large vocabulary recognition engine. In INTERSPEECH, pages 1691-1694, 2001.
19. O. Lopes, M. Pousa, S. Escalera, and J. Gonzalez. Multi Hand Pose Recognition System using Kinect Depth Sensor. In ICPR Workshop on Gesture Recognition, 2012.
20. A. Malima, E. Özgür, and M. Çetin. A fast algorithm for vision-based hand gesture recognition for robot control. IEEE 14th Conference on Signal Processing and Communications Applications, 2006.
21. C. M. Mateo, P. Gil, J. A. Corrales, S. T. Puente, and F. Torres. RGBD Human-Hand recognition for the Interaction with Robot-Hand. In IROS, 2012.
22. R. Memisevic and G. Hinton. Unsupervised learning of image transformations. In CVPR, 2007.
23. M. A. Mendoza and N. P. de la Blanca. Applying Space State Models in Human Action Recognition: A Comparative Study. In 5th International Conference on Articulated Motion and Deformable Objects, pages 53-62, 2008.
24. L. Nguyen, J.-M. Odobez, and D. Gatica-Perez. Using Self- Context for Multimodal Detection of Head Nods in Face-to- Face Interactions. In ICMI, 2012.
25. E. Ohn-Bar and M. M. Trivedi. Joint Angles Similiarities and HOG for Action Recognition. In CVPR Workshop on Human Activity Understanding from 3D Data (HAU3D13), pages 465-470, 2013.
26. I. Oikonomidis, N. Kyriazis, and A. Argyros. Efficient model-based 3D tracking of hand articulations using Kinect. In BMVC, pages 101.1-101.11, 2011.
27. M. Ranzato, F. J. Huang, Y.-L. Boureau, and Y. LeCun. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. In CVPR, 2007.
28. B. Rimé, L. Schiaratura, M. Hupet, and A. K. S. Ghysselinckx. Effects of relative immobilization on the speaker's nonverbal behavior and on the dialogue imagery level. Motivation and Emotion, 8, 1984.
29. P. Scovanner, S. Ali, and M. Shah. A 3-Dimensional SIFT Descriptor and its Application to Action Recognition. In 15th International Conference on Multimedia, 2007.
30. E. Stergiopoulou and N. Papamarkos. Hand gesture recognition using a neural network shape fitting technique. Engineering Applications of Artificial Intelligence, 22(8):1141-1158, Dec. 2009.
31. I. Sutskever and G. Hinton. Learning Multilevel Distributed Representations for High-Dimensional Sequences. In 11th International Conference on Artificial Intelligence and Statistics, 2007.
32. G. Taylor, G. Hinton, and S. Roweis. Modeling Human Motion Using Binary Latent Variables. In NIPS, 2006.
33. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. IJCV, 103(1):60-79, 2013.
34. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatio-temporal features for action recognition. BMVC, pages 124.1-124.11, 2009.
35. R. Wang and J. Popović. Real-Time Hand-Tracking with a Color Glove. In SIGGRAPH, 2009.
36. D.Weinland, R. Ronfard, and E. Boyer. A Survey of Vision- Based Methods for Action Representation, Segmentation and Recognition. Technical report, INRIA, 7212, 2010.
37. G. Willems, T. Tuytelaars, and L. V. Gool. An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector. In ECCV, pages 650-663, 2008.
38. M.-H. Yang and N. Ahuja. Recognizing Hand Gesture Using Motion Trajectories. In CVPR, 1999.
39. Y.Tian, R.Sukthankar, and M.Shah. Spatiotemporal Deformable Part Models for Action Detection. InCVPR, 2013.
40. Y. Zhu, W. Chen, and G. Guo. Fusing Spatiotemporal Features and Joints for 3D Action Recognition. In CVPR Workshop on Human Activity Understanding from 3D Data (HAU3D13), pages 486-491, 2013.