With the recent advancements in computer vision, Human Action Recognition has become one of the aesthetic research fields. There has been little research work in the real world conditions of human action recognition systems, which encourages us to seriously search in this application domain. Although lot of healthy approaches have been introduced in the literature, they are still insufficient to fully cover the challenges. This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity recognition and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods.