In this paper, we propose a local feature-based human motion analysis framework. Instead of using traditional analysis methods to characterize the global structure of human motion, we extract features directly from local regions that contain motion. To implement the above concept, we adopt the rules of visual attention theory, which assert that a human motion can be described simply by a set of local features comprised of spatial relationships rather than human postures. We select two kinds of features to represent the local variation of a human motion. First, we extract the long-term movement trend of the motion. The second feature is actually a set of rough features derived by sampling multi-scale moving edges. The two types of features are considered together during the recognition process. Our experiments demonstrate that the proposed approach can achieve very good recognition results.