A Robust Frame-Sequential Video Segmentation Method

Accurate video object segmentation is still a difficult task so far due to the following reasons: the inseparable color between foreground and background (F/B) scenes is hard to segment only by color model, Occlusion/disocclusion caused by large movement, new exposed regions and topology change often breaks the continuity of video, Lastly, accurately building color model plays a critical role in obtaining a satisfactory result, which has often been ignored by people. In our method, all these problems have been considered: A kind of motion prediction method based on local coherence is adopted to separate the inseparable color. Then a self-adapting distance support model is used to build the object color by which our model can become much more robust against occlusion/disocclusion and color confusion. At last the final probability is generated by fusing all the clues, and the binary segmentation is finished by 3D Graph-Cut optimization. The experimental results are presented to demonstrate the effectiveness of the proposed method at achieving high quality results, as well as the robustness of the proposed method against several challenging test inputs.