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This paper presents a robust automated noninvasive video monitoring approach to recover the human pose in conditions with persistent heavy obscuration. The proposed methods are compared with Ramanan's stylized pose detection method and Wang's sequential pose model. The experimental results show that the proposed method performs significantly better than Ramanan's approach, is able to estimate the obscured body pose with various postures and obscuration levels in different environments, and is not sensitive to illumination changes. The system is evaluated in two domains: sleeping human subjects obscured by a bed cover, and pedestrians with a cluttered background scene, low feature contrast and baggy clothing. The body part detectors are trained in the sleep monitoring domain but are still able to estimate the pose in the pedestrian domain, demonstrating the robustness of the proposed technique.