Silhouette analysis for human action recognition based on maximum spatio-temporal dissimilarity embedding
In this paper, we present a human action recognition method for human silhouette sequences. Inspired by the locality preserving projection and its variants, a novel manifold embedding method, maximum spatio-temporal dissimilarity embedding, is proposed to embed each action frame into a manifold, where frames from different action classes can be well separated. Unlike existing methods that incorporate both inter-class and intra-class information in the embedding process, our proposed method focuses on maximizing distances between frames that are similar in appearance but are from different classes and takes the temporal information into consideration. A variant of Hausdorff distance is introduced for frame and sequence classifications. Extensive experimental results and comparison with state-of-the-art methods demonstrate the effectiveness and robustness of the proposed method for human action silhouette analysis.
silhouette analysis human action recognition hausdorff distance gait recognition manifold learning