RBM-based Silhouette Encoding for Human Action Modelling

In this paper we evaluate the use of Restricted Bolzmann Machines (RBM) in the context of learning and recognizing human actions. The features used as basis are binary silhouettes of persons. We test the proposed approach on two datasets of human actions where binary silhouettes are available: ViHASi (synthetic data) and Weizmann (real data). In addition, on Weizmann dataset, we combine features based on optical flow with the associated binary silhouettes. The results show that thanks to the use of RBM-based models, very informative and shorter feature vectors can be obtained for the classification tasks, improving the classification performance.