Human Action Recognition Using Latent-Dynamic Condition Random Fields

In video human action recognition of the continual human motion is a difficult point for application. The method of human action recognition based on latent-dynamic condition random fields is presented. By star form distance descriptor of human body contour, human pose is extracted. Then in continuous sequences method building the model of LDCRF shows the mapping relation between action feature and action semantics. Comparing with traditional CRF and HCRF, by designing the affiliation of latent feature and human pose, LDCRF implements the modeling in internal action and external movement feature. In the experiment, Weizmann action database is used, and three experiments are designed. When composition continuous sequence is tested, except “skip” action, recognition rate reaches over 90%; receiver operating characteristic of three model shows LDCRF moels have the better descriptive capability in internal action and external movement feature;while human action is affected by angle, accessory and occlusion. It shows LDCRF is robustness in the human body contour integrity situation.