Action recognition based on kernel machine encoding qualitative prior knowledge

This paper proposes a recognition algorithm based on kernel classifier for human daily life action such as walking or lying down. The advantage of the proposed algorithm is to realize implant of qualitative human knowledge and robust recognition accuracy at the same time. The main features of the presented method are: (1)utilizing Gaussian process with latent variables for relation between recognized labels and input human motion, (2) in order to embed prior knowledge for proper recognition of novel motion dissimilar to the learned motion data, assigning probabilistic labels to virtual human motions generated in "sparse" area of input motion feature space, (3) learning parameters of classifier by real human motion with labels and the virtual motions in Bayesian perspective. The result of cross-validation like experiment shows that the accuracy of the proposed method is as good as support vector classification based recognition methods. It is also shown that the proposed method can recognize some novel motion fit into human common sense even when the classifiers without embedded knowledge fails to recognize it.