Human activities segmentation and location of key frames based on 3D skeleton

Human activities consist of multiple simple actions, and the temporal information benefit action recognition at all time scales. Considering energy information of human action as action similarity criterion, we present a temporal segmentation method which action videos are firstly segmented to atomic actions based on kinematics information of human skeleton, then the atomic action units are iteratively incorporated in meaningful group by considering similarity of energy information. And the key frames are located at sphere of maximum energy information. We tested our method on two challenging datasets and its performance is better than other state of the art methods.