Vision-based human action recognition is currently one of the most active research fields. It has many promising applications such as intelligent surveillance

perceptual interface and content-based video retrieval. Even though

some hurdles still slower the development of action recognition

which the actions are often observed from arbitrary camera viewpoints in realistic scene. So view-invariance is important for action recognition

growing number of research groups to pay more attentions to research related to the view-invariant issue. This paper provides a survey on view-invariant recognition of poses and actions. The improvements of this topic in the last several years are discussed in detail from four aspects: space-time based methods

state-space approaches

dimension reduction based methods

and trajectories based methods. Public available standard datasets are presented

and the concluding discussion assesses the progress so far

and outlines some future directions.