Fast video object segmentation using affine motion and gradient-based color clustering
Video object segmentation is an important component for object-based video coding schemes such as MPEG-4. A fast and robust video segmentation technique, which aims at efficient foreground and background separation via effective combination of motion and color segmentation modules is proposed in this work. First, a non-parametric gradient-based iterative color clustering algorithm called the mean shift algorithm is employed to provide a robust initial dominant color regions according to color similarity. With the dominant color information obtained from previous frames as an initial seed for the next frame, we can reduce the amount of computational time by 50%. Next, moving regions are identified by a motion detection method based on the frame intensity difference, which helps to circumvent the motion estimation complexity for the whole frame. Only moving regions are further merged or split according to the region-based affine motion model. Furthermore, sizes, colors, and motion information of homogeneous regions are tracked to increase temporal and spatial consistency of extracted objects. The proposed system is evaluated for several typical MPEG-4 test sequences, and it provides very consistent and accurate object boundaries throughout the entire test sequences.