Matching shape sequences in video with applications in human movement analysis

TitleMatching shape sequences in video with applications in human movement analysis
Publication TypeJournal Articles
Year of Publication2005
AuthorsA V, Roy-Chowdhury AK, Chellappa R
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination1896 - 1909
Date Published2005/12//
ISBN Number0162-8828
KeywordsAutomated;Photography;Reproducibility of Results;Sensitivity and Specificity;Subtraction Technique;Video Recording;, autoregressive moving average model;deforming shapes;dynamic time-warping;feature extraction;gait-based human recognition;human movement analysis;matching shape sequences;video;autoregressive moving average processes;feature extraction;image matching;imag, Biological;Models, Computer-Assisted;Imaging, Statistical;Movement;Pattern Recognition, Three-Dimensional;Leg;Models

We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall's definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on dynamic time-warping. We suggest a modification of the dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication.