%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2009/08.17.22.40
%2 sid.inpe.br/sibgrapi@80/2009/08.17.22.40.23
%@doi 10.1109/SIBGRAPI.2009.29
%T Action recognition in video by covariance matching of silhouette tunnels
%D 2009
%A Guo, Kai,
%A Ishwar, Prakash,
%A Konrad, Janusz,
%@affiliation Boston University
%@affiliation Boston University
%@affiliation Boston University
%E Nonato, Luis Gustavo,
%E Scharcanski, Jacob,
%B Brazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
%C Rio de Janeiro, RJ, Brazil
%8 11-14 Oct. 2009
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K video analysis, action recognition, silhouette tunnel, covariance matching, generalized eigenvalues.
%X Action recognition is a challenging problem in video analytics due to event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. Central to these challenges is the way one models actions in video, i.e., action representation. In this paper, an action is viewed as a temporal sequence of local shape-deformations of centroid-centered object silhouettes, i.e., the shape of the centroid-centered object silhouette tunnel. Each action is represented by the empirical covariance matrix of a set of 13-dimensional normalized geometric feature vectors that capture the shape of the silhouette tunnel. The similarity of two actions is measured in terms of a Riemannian metric between their covariance matrices. The silhouette tunnel of a test video is broken into short overlapping segments and each segment is classified using a dictionary of labeled action covariance matrices and the nearest neighbor rule. On a database of 90 short video sequences this attains a correct classification rate of 97%, which is very close to the state-of-the-art, at almost 5-fold reduced computational cost. Majority-vote fusion of segment decisions achieves 100% classification rate.
%@language en
%3 IEEE-PID949748_final_submission.pdf