Fast and accurate moving object extraction technique for MPEG-4 object-based video coding

A fast and robust video segmentation technique is proposed to generate a coding optimized binary object mask in this work. The algorithm exploits the color information in the L*u*v* space, and combines it with the motion information to separate moving objects from the background. A non-parametric gradient- based iterative color clustering algorithm, called the mean shift algorithm, is first employed to provide robust homogeneous color regions according to dominant colors. Next, moving regions are identified by a motion detection method, which is developed based on the frame intensity difference to circumvent the motion estimation complexity for the whole frame. Only moving regions are analyzed by a region-based affine motion model, and tracked to increase the temporal and spatial consistency of extracted objects. The final shape is optimized for MPEG-4 coding efficiency by using a variable bandwidth region boundary. The shape coding efficiency can be improved up to 30% with negligible loss of perceptual quality. The proposed system is evaluated for several typical MPEG-4 test sequences. It provides consistent and accurate object boundaries throughout the entire test sequences.