Fully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information

In this paper, a star-skeleton-based methodology is described for analyzing the motion of a human target in a video sequence. Star skeleton is a fast skeletonization technique by connecting centroid of target object to its contour extremes. We represent the skeleton as a five-dimensional vector, which includes information about the positions of head and four limbs of a human shape in a given frame. In this manner, an action is composed of a sequence of star skeletons. With the purpose of use an HMM which allows model the actions, a posture codebook is built integrating star skeleton and motion information. With this last information we can distinct better between actions. Supervised (manual) and No-supervised methods (clustering-based methodology) have been used to create the posture codebook. The codebook is dependently of the actions to represent (We choose four actions as example: walk, jump, wave and jack). Obtained results show, firstly, including motion information is important to get a correctly differentiation between actions. On the other hand, using a clustering methodology to create the codebook causes a substantial improvement in results.