A segmentation system with model-assisted completion of video objects

This paper presents a new algorithm for video-object segmentation, which combines motion-based segmentation, high-level object-model detection, and spatial segmentation into a single framework. This joint approach overcomes the disadvantages of these algorithms when applied independently. These disadvantages include the low semantic accuracy of spatial segmentation and the inexact object boundaries obtained from object-model matching and motion segmentation. The now proposed algorithm alleviates three problems common to all motion-based segmentation algorithms. First, it completes object areas that cannot be clearly distinguished from the background because their color is near the background color. Second, parts of the object that are not considered to belong to the object since they are not moving, are still added to the object mask. Finally, when several objects are moving, of which only one is of interest, it is detected that the remaining regions do not belong to any object-model and these regions are removed from the foreground. This suppresses regions erroneously considered as moving or objects that are moving but that are completely irrelevant to the user.