Semantic Human Action Classification Based on Energy-Action Model

Human action classification for interpreting at a semantic level has been and still a highly interesting and important research topic. The research results are capable of analyzing human action that we visually perceive in many aspects. Therefore the research requires an effective and competent approach to accurately interpret human action. In this paper, we present a novel model called the energy-action (EA) model for producing more semantic classification. The EA model is based on the fundamental concepts of biomechanics that human movement in different classes is likely to spend different amounts of energy. The EA model is classified by using feedforward backpropagation neural network (FBNN) to obtain from the 5-fold cross validation. Experimental results show that the proposed provides much more authentic meaning of human actions