Recognizing human actions based on silhouette energy image and global motion description

In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and variability models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending state of an action), camera observations (distance from camera, slanting motion, and rotation of human body), and view variations are proposed. We construct the variability models based on SEI and the variability parameters. The global shape-based motions express the spatio-temporal properties of SEI and variability models. Our construction of the optimal model for each action and view is based on the support vectors of motion descriptions of combined action models. We recognize different daily human actions of different styles successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.