Existing video monitoring techniques require clinicians to analyze substantial amounts of video data in diagnosis of sleep apnea. Analysis of the covered human body from video is a challenging task as traditional computer vision methods such as correlation, template matching, background subtraction, contour models and related techniques for object tracking become ineffective because of the large degree of occlusion for long periods. In condition of persistent heavy occlusion, difficulties arise from night vision, large variances of image features according to the occlusion level, the shifting of the cover surface with movements, obscuration of the bodiespsila edges by the cover, and wrinkle noises. We propose a near real time method to robustly estimate the pose of fully/partially covered or uncovered human body. The proposed method contains a novel weak human model to accommodate large variances of image features and a strong pose recognition model derived from a stylized pose detector used for people tracking by Ramanan et al.. We improve the stylized pose detection model by modifying the cost formula and template representation to overcome weak cues and strong noise due to heavy occlusion. In evaluation, the experimental results show that the proposed model is promising to estimate the pose of a human body with fully or partially covered or without covered.