Selecting relevant features for human motion recognition

Recently, there is a growing interest in automatic recognition of human motion for applications, such as humanoid robots, human activity monitoring, and surveillance. In this paper we investigate motion recognition based on joint angle trajectories derived from marker-based video recordings. The goal of this paper is to improve the generalization and robustness of human motion recognition even if only limited amount of training data is available. We achieve this goal by significantly reducing the amount of input features. We leverage on recent studies in the area of neuroscience which indicate that human motions display only a few independent degrees of freedom (DOF). We examine which DOF are relevant for recognizing upper body human motions and to what extend the dimensionality of the feature vectors can be reduced in order to simplify the data acquisition and improve the robustness of the recognition process. Our final results indicate that careful selection of features proves to reduce the number of features by a factor of up to 3, while at the same time significantly improving the recognition performance.