Human action recognition constitutes a core component of
advanced human behavior analysis. The detection and recognition of basic human motion enables to analyze and understand human activities, and to react proactively providing different kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimization for human action recognition is proposed. The resulting recognition rate and computational cost are significantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection.
The introduced feature is computed using only the contour
points of human silhouettes. These are spatially aligned
based on a radial scheme. This definition shows to be proficient for feature subset selection, since different parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.