Human detection is an essential issue in image processing applications such as safety monitoring, interactive game, robot control, etc. However, most of current approaches focus on detecting pedestrians. In this paper, motivated by the high achievements of poselet technique, we propose a combinatorial approach between poselet technique and articulated pose estimation method to provide a robust hybrid structure for human detection in various environments. Also, a novel foreground extraction method, named edge based RPCA, is introduced to handle with the uncertain environments such as high illumination change or camera shaking problem. Then the candidate containing small foreground regions are discarded. In addition, an updating approach for the current human boundaries between consecutive frames are applied to prevent the abrupt change. To verify our proposed approach, the human detection experiments are conducted on the self-recorded database for both indoor and outdoor areas.