TY - GEN
T1 - Multiple timescale recurrent neural network with slow feature analysis for efficient motion recognition
AU - Kim, Jihun
AU - Jeong, Sungmoon
AU - Yu, Zhibin
AU - Lee, Minho
PY - 2013
Y1 - 2013
N2 - Multiple Timescale Recurrent Neural Network (MTRNN) model is a useful tool to learn and regenerate various kinds of action. In this paper, we use MTRNN as a dynamic model to analyze different human motions. Prediction error from dynamic model is used to classify different human actions. However, it is difficult to fully cover the human actions depending on the speed using dynamic model. In order to overcome the limitation of dynamic model, we considered Slow Feature analysis (SFA) which is used to extract the unique slow features from human actions data. In order to make input training data, we obtain 3 kinds of human actions by using KINECT. 3 dimensional slow feature data is be extracted by using SFA and those SFA feature data are used as the input of MTRNN for classification. The experiment results show that our proposed model performs better than the traditional model.
AB - Multiple Timescale Recurrent Neural Network (MTRNN) model is a useful tool to learn and regenerate various kinds of action. In this paper, we use MTRNN as a dynamic model to analyze different human motions. Prediction error from dynamic model is used to classify different human actions. However, it is difficult to fully cover the human actions depending on the speed using dynamic model. In order to overcome the limitation of dynamic model, we considered Slow Feature analysis (SFA) which is used to extract the unique slow features from human actions data. In order to make input training data, we obtain 3 kinds of human actions by using KINECT. 3 dimensional slow feature data is be extracted by using SFA and those SFA feature data are used as the input of MTRNN for classification. The experiment results show that our proposed model performs better than the traditional model.
KW - Motion recognition
KW - Multiple timescale recurrent neural network
KW - Slow feature analysis
UR - http://www.scopus.com/inward/record.url?scp=84893428240&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42042-9_41
DO - 10.1007/978-3-642-42042-9_41
M3 - Conference contribution
AN - SCOPUS:84893428240
SN - 9783642420412
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 323
EP - 330
BT - Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
T2 - 20th International Conference on Neural Information Processing, ICONIP 2013
Y2 - 3 November 2013 through 7 November 2013
ER -