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A statistical model-based video segmentation algorithm is presented for head-and-shoulder type video. This algorithm uses domain knowledge by abstracting the head-and-shoulder object with a blob-based statistical region model and a shape model. The object segmentation problem is then converted into a model detection and tracking problem. At the system level, a hierarchical structure is designed and spatial and temporal filters are used to improve segmentation quality. This algorithm runs in real time over a QCIF size video, and segments it into background, head and shoulder three video objects on average Pentium PC platforms. Due to its real time feature, this algorithm is appropriate for real time multimedia services such as videophone and web chat. Simulation results are offered to compare MPEG-4 performance with H.263 on segmented video objects with respects to compression efficiency, bit rate adaptation and functionality.