Action Recognition with Adaptive RBFNN

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.