This paper presents a method for recognizing human actions based on pose primitives. In learning mode, the parameters representing poses and activities are estimated from videos. In run mode, the method can be used both for videos or still images. For recognizing pose primitives, we extend a Histogram of Oriented Gradient (HOG) based de- scriptor to better cope with articulated poses and cluttered background. Action classes are represented by histograms of poses primitives. For sequences, we incorporate the lo- cal temporal context by means of n-gram expressions. Ac- tion recognition is based on a simple histogram compari- son. Unlike the mainstream video surveillance approaches, the proposed method does not rely on background subtrac- tion or dynamic features and thus allows for action recog- nition in still images.