Online action recognition with wrapped boosting

In this paper, we propose wrapped boosting that is extension of boosting algorithm for robust online action recognition. Boosting algorithm is one of ensemble learning algorithm and is also known as a feature selector. In our previous work utilizing boosting, we achieved automatic feature selection and robust model-based action classifiers which had very small calculation cost based on posture information of human body joints. However, which joints we should allocate posture sensors to must be given by humans in advance. Our new learning framework of wrapped boosting provides not only automatic feature selection but also automatic sensor allocation to proper joints of humans for target actions. We evaluated our algorithm targeting gait motion based on motion data fetched by motion capturing system. In consequence, wrapped boosting was able to select proper joints to which limited sensors should be attached, and to construct more robust classifiers compared to constructing classifiers with all joints available. Classifiers constructed only with existing boosting algorithm were subject to over-fitting to training data.