Global and local motion descriptions for human action recognition

In this paper, we present a method of human action recognition that uses spatial-temporal local and global motion descriptions from the image sequence with selected variability. The local motions use the dense optical flow velocity of the image sequence. The principal components are extracted from the silhouette image sequence of an action and are regarded as global motions. In order to address the variability, several parameters, such as anthropometry of the person, phase, camera observations (zoom, tilt, and rotation of the human body), and variations in view are proposed. We use support vector machine for learning and recognizing the actions. We successfully recognize some daily life human actions in the indoor and outdoor environment and our proposed method of human action recognition is robust and efficient.