Human detection is a challenging problem in video processing, which is applied in many fields: robot control, surveillance system, traffic tracking etc. Recently, there have been many publications involving this problem. However, most of methods still focus on pedestrian detection. In this paper, based on the poselet techniques, we introduce a new method to detect human in video under various environments. By combining poselet and gradient local auto-correlation classifier, we propose an efficient technique in human detection and reduce false detection. Also, focused on edge-based robust principal component analysis, a new foreground extraction method is developed to handle the ambiguous environment such as: leaf motion, illumination etc. By applying the proposed method, the small motion artifacts can be rejected. Experimental results show that our method has the high accuracy in various environments.