In this paper we discuss foreground detection and human body silhouette extraction and tracking in monocular video systems designed for human motion analysis applications. Vision algorithms face many challenges when it comes to analyze human activities in non-controlled environments. For instance, issues like illumination changes, shadows, camouflage and occlusions make the detection and the tracking of a moving person a hard task to accomplish. Hence, advanced solutions are required to analyze the content of video sequences. We propose a real-time, two-level foreground detection, enhanced by body parts tracking, designed to efficiently extract person silhouette for monocular video-based human motion analysis systems. We aim to find solutions for different non-controlled environment challenges, which make the detection and the tracking of a moving person a hard task to accomplish. On the first level, we propose an enhanced Mixture of Gaussians, built on both chrominance-luminance and chrominance-only spaces, which handles global illumination changes. On the second level, we improve segmentation results, in interesting areas, by using statistical foreground models updated by a high-level tracking of body parts. Each body part is represented with a set of template characterized by a feature vector built in an initialization phase. Then, high level tracking is done by finding blob-template correspondences via distance minimization in feature space. Correspondences are then used to update foreground models, and a graph cut algorithm, which minimizes a Markov random field energy function containing these models, is used to refine segmentation. We were able to extract a refined silhouette in the presence of light changes, noise and camouflage. Moreover, the tracking approach allowed us to infer information about the presence and the location of body parts even in the case of partial occlusion.