Title :
Action recognition with adaptive RBFNN
Author :
Aphaipanan, Srisuda ; Kidjaidure, Yuttana
Author_Institution :
Dept. of Electron., King Mongkut´s Inst. of Technol. Ladkrabang (KMITL), Bangkok, Thailand
Abstract :
This paper presents a method for action recognition by Adaptive Radial Basis Function Neural Network (ARBFNN) based on 3 dimensional human models. Recently, the action recognition of human is popular for the interactive applications caused many researchers tried to develop the algorithm and to find the features that have high performance. So this paper employed the features from the scalar part of Quaternion rotation that uses lower dimension than the conventional Cartesian features. Also, the Fuzzy C Means technique was used for pre-training the Radial Basis Function Neural Network (RBFNN). This method was tested with the CMU MoCap database and showed high recognition rates with small computation time.
Keywords :
fuzzy set theory; image motion analysis; image recognition; learning (artificial intelligence); radial basis function networks; 3 dimensional human models; ARBFNN; CMU MoCap database; RBFNN pretraining; action recognition; adaptive RBFNN; adaptive radial basis function neural network; fuzzy C means technique; interactive applications; quaternion rotation; radial basis function neural network pretraining; Classification algorithms; Clustering algorithms; Hidden Markov models; Joints; Quaternions; Three-dimensional displays; Vectors; Fuzzy C Means; Quaternion; Radial Basis Function; pre-training;
Conference_Titel :
Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), 2014 4th Joint International Conference on
Conference_Location :
Chiang Rai
Print_ISBN :
978-1-4799-3854-4
DOI :
10.1109/JICTEE.2014.6804095
