Human action recognition based on layered-HMM

We address the problem of human action understanding of the upper human body from video sequences. Time-sequential images expressing human actions are transformed to sequences of feature vectors containing the configuration of the human body. A human is modeled as a collection of body parts, linked in a kinematic structure. The relation of the joints is used to estimate the human pose. A proposed layered HMM framework decomposes the human action recognition problem into two layers. The first layer models the actions of two arms individually from low-level features. The second layer models the interrelationship of two arms as an action. Experiments with a set of six types of human actions demonstrate the effectiveness of our proposed scheme, and the comparisons with other HMM systems show the robustness.