Human posture recognition using multi-scale morphological method and Kalman motion estimation

For the purpose of estimating the posture parameters of a moving human body in visual surveillance applications, we present a model-based shape analysis method and convert the problem to the optimal matching of 2D silhouettes of the human body in parametric shape space. We point out the causality of shape similarity in morphological scale space. Based on this causality and after using the α-β-γ filter to estimate the initial value of the matching, the method we proposed works in a coarse-to-fine manner on silhouettes practically segmented from a complex environment, and can converge fast enough to meet real-time requirements