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Action recognition with adaptive RBFNN
Author(s)
Aphaipanan, Srisuda
Kidjaidure, Yuttana
Date Issued
January 1, 2014
Type
Conference Paper
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. © 2014 IEEE.
Citation
Jictee 2014 4th Joint International Conference on Information and Communication Technology Electronic and Electrical Engineering, 2014