Real-time video object segmentation for MPEG encoded video sequences

We propose a real-time object segmentation method for MPEG encoded video. Computational superiority is the main advantage of compressed domain processing. We exploit the macro-block structure of the encoded video to decrease the spatial resolution of the processed data, which exponentially reduces the computational load. Further reduction is achieved by temporal grouping of the intra-coded and estimated frames into a single feature layer. In addition to computational advantage, compressed-domain video possesses important features attractive for object analysis. Texture characteristics are provided by the DCT coefficients. Motion information is readily available without incurring cost of estimating a motion field. To achieve segmentation, the DCT coefficients for I-frames and block motion vectors for P-frames are combined and a frequency-temporal data structure is constructed. Starting from the blocks where the ac-coefficient energy and local inter-block dc-coefficient variance is small, the homogeneous volumes are enlarged by evaluating the distance of candidate vectors to the volume characteristics. Affine motion models are fit to volumes. Finally, a hierarchical clustering stage iteratively merges the most similar parts to generate an object partition tree as an output.