Constrained video object segmentation by color masks and MPEG-7 descriptors

We present an automatic and computationally conservative boundary extraction method using available priori information in a framework consists of change detection mask, region growing, and trajectory motion. Instead of segmenting a entire video frame, only the regions belong to a target object specified by a set of rules are detected. One example of such rules is skin color features of a human body part. Processing domain is limited to the pixels that satisfy color or geometric rules. These rules are represented as a detection mask and implemented as a look-up table. As a result, significant computational reduction and real-time performance are achieved. The framework utilizes color consistency within a centroid-linkage growing technique to grow initial regions. Region seeds are selected among the pixels in the detection mask. The similarity thresholds are adapted from the MPEG-7 dominant color descriptors. The segmentation results of a frame diffused to the next frame and region statistics such as trajectory, percentage of the changed pixels, etc., are registered to determine the moving regions. A computational load comparison of the constrained region growing and regular region growing shows significant reduction in the complexity.