A Priori Data and A Posteriori Decision Fusions for Human Action Recognition

In this paper, we tackle the challenge of human action recognition using multiple data sources by mixing a pri-ori data fusion and a posteriori decision fusion. Our strategy applied from 3 main classifiers (Dynamic Time Warping, Multi-Layer Perceptron and Siamese Neural Network) using several decision fusion methods (Voting , Stacking, Dempster-Shafer Theory and Possibility Theory) on two databases (MHAD (Ofli et al., 2013) and ChAirGest (Ruffieux et al., 2013)) outperforms state-of-the-art results with respectively 99.85% ± 0.53 and 96.40% ± 3.37 of best average correct classification when evaluating a leave-one-subject-out protocol.