Supervised multiple timescale recurrent neuron network model for human action classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8227 LNCS, Issue PART 2, 2013, Pages 196-203

  Yu, Zhibin ^a   Mallipeddi, Rammohan ^a   Lee, Minho ^a  

^a Kyungpook National University   (South Korea)

Author keywords

Classification; Continuous time scales recurrent neuron network; Human action; SMTRNN

Indexed keywords

CLASSIFICATION TASKS; DYNAMIC TASKS; HUMAN ACTION CLASSIFICATIONS; HUMAN ACTIONS; INITIAL STATE; MULTIPLE TIMESCALES; NEURON NETWORKS; SMTRNN;

CLASSIFICATION (OF INFORMATION); DATA PROCESSING;

RECURRENT NEURAL NETWORKS;

EID: 84893410167     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-42042-9_25     Document Type: Conference Paper

References (15)

1. Arie, H., Arakaki, T., Sugano, S., Tani, J.: Imitating others by composition of primitive actions: A neuro-dynamic model. Robotics and Autonomous Systems 60(5), 729-774 (2012)
2. Cruz, L., Djalma, L., Luiz, V.: Kinect and RGBD Images: Challenges and Applications Graphics. In: 2012 25th SIBGRAPI Conference on Patterns and Images Tutorials (SIBGRAPI-T), August 22-25 (2012)
3. Doya, K., Yoshizawa, S.: Adaptive neural oscillator using continuous-time backpropagation learning. Neural Network 2, 375-386 (1989)
4. Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous timerecurrent neural networks. Neural Networks 6(6), 801-806 (1993)
5. Hinoshita, W., Arie, H., Tani, J., Okuno, H.G., Ogata, T.: Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network. Neural Networks 24(4), 311-320 (2011)
6. Husken, M., Stagge, P.: Recurrent Neural Networks for Time Series Classification. Neuro Computing 50(C), 223-235 (2003)
7. Jeong, S., Arie, H., Lee, M., Tani, J.: •Neuro-robotics study on integrative learning of proactive visual attention and motor behaviors. Cogn. Neurodyn. 6, 43-59 (2011, 2012)
8. Joslin, C., El-Sawah, A., Chen, Q., Georganas, N.: Dynamic Gesture Recognition. In: IMTC 2005 - Instrumentation and Measurement Technology Conference, Ottawa, Canada, May 17-19 (2005)
9. Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976-990 (2010)
10. Sakai, K., Kitaguchi, K., Hikosaka, O.: Chunking during human visuomotor sequence learning. Exp. Brain Res. 152, 229-242 (2003)
11. Tani, J., Nishimoto, R., Paine, R.: Achieving "organic compositionality" throughselforganization: reviews on brain-inspired robotics experiments. NeuralNetworks 21, 584-603 (2008)
12. Thomas, K., Andre, B., Michalis, F., KcC: HMM-based Human Motion Recognition with Optical Flow Data. In: 9th IEEE-RAS International Conference on Humanoid Robot, Paris, France, December 7-10 (2009)
13. Thoroughman, K.A., Shadmehr, R.: Learning of action through adaptive combination of motor primitives. Science 407, 742-747 (2000)
14. YamashitaY., T.J.: Emergence of functional hierarchy in a multiple timescaleneural network model: a humanoid robot experiment. PLoS ComputationalBiology 4(11) (2008)
15. Zhang, Y., Ogata, T., Takahashi, T., Okuno, H.G.: Dynamic Recognition of Environmental Sounds with Recurrent Neural Network. In: The 28th Annual Conference of the Robotics Society of Japan, Nagoya, Japan (2010)