Making feature selection for human motion recognition more interactive through the use of taxonomies

Human activity recognition is an essential ability for service robots and other robotic systems which interact with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if real time constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a novel approach for the integration of a feature selection in human motion recognition is proposed. Typically, the features are chosen with respect to the relevance of the features for the classification of the activity which shall be recognized. Our new approach extends this process by involving background knowledge about the features and active user engagement. Using taxonomies built on the complete feature set, users can be provided with an interface to guide and refine the selection process. Thereby, certain problems can be avoided which are common if noisy or small amounts of training data are used to train the system.